{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 推荐系统组队学习 第二次打卡：数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数据分析的价值主要在于熟悉了解整个数据集的基本情况包括每个文件里有哪些数据，具体的文件中的每个字段表示什么实际含义，以及数据集中特征之间的相关性，在推荐场景下主要就是分析用户本身的基本属性，文章基本属性，以及用户和文章交互的一些分布，这些都有利于后面的召回策略的选择，以及特征工程。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入相关包\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.rc('font', family='SimHei', size=13)\n",
    "\n",
    "import os,gc,re,warnings,sys\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 首先是读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './data_raw/'\n",
    "\n",
    "#####train\n",
    "trn_click = pd.read_csv(path+'train_click_log.csv')\n",
    "item_df = pd.read_csv(path+'articles.csv')\n",
    "item_df = item_df.rename(columns={'article_id': 'click_article_id'})  #重命名，方便后续match\n",
    "item_emb_df = pd.read_csv(path+'articles_emb.csv')\n",
    "\n",
    "#####test\n",
    "tst_click = pd.read_csv(path+'testA_click_log.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对每个用户的点击时间戳进行排序\n",
    "trn_click['rank'] = trn_click.groupby(['user_id'])['click_timestamp'].rank(ascending=False).astype(int)\n",
    "tst_click['rank'] = tst_click.groupby(['user_id'])['click_timestamp'].rank(ascending=False).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算用户点击文章的次数，并添加新的一列count\n",
    "trn_click['click_cnts'] = trn_click.groupby(['user_id'])['click_timestamp'].transform('count')\n",
    "tst_click['click_cnts'] = tst_click.groupby(['user_id'])['click_timestamp'].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>click_environment</th>\n",
       "      <th>click_deviceGroup</th>\n",
       "      <th>click_os</th>\n",
       "      <th>click_country</th>\n",
       "      <th>click_region</th>\n",
       "      <th>click_referrer_type</th>\n",
       "      <th>rank</th>\n",
       "      <th>click_cnts</th>\n",
       "      <th>category_id</th>\n",
       "      <th>created_at_ts</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199999</td>\n",
       "      <td>160417</td>\n",
       "      <td>1507029570190</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>281</td>\n",
       "      <td>1506942089000</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>199999</td>\n",
       "      <td>5408</td>\n",
       "      <td>1507029571478</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1506994257000</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199999</td>\n",
       "      <td>50823</td>\n",
       "      <td>1507029601478</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>99</td>\n",
       "      <td>1507013614000</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199998</td>\n",
       "      <td>157770</td>\n",
       "      <td>1507029532200</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>281</td>\n",
       "      <td>1506983935000</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>199998</td>\n",
       "      <td>96613</td>\n",
       "      <td>1507029671831</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>209</td>\n",
       "      <td>1506938444000</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  click_article_id  click_timestamp  click_environment  \\\n",
       "0   199999            160417    1507029570190                  4   \n",
       "1   199999              5408    1507029571478                  4   \n",
       "2   199999             50823    1507029601478                  4   \n",
       "3   199998            157770    1507029532200                  4   \n",
       "4   199998             96613    1507029671831                  4   \n",
       "\n",
       "   click_deviceGroup  click_os  click_country  click_region  \\\n",
       "0                  1        17              1            13   \n",
       "1                  1        17              1            13   \n",
       "2                  1        17              1            13   \n",
       "3                  1        17              1            25   \n",
       "4                  1        17              1            25   \n",
       "\n",
       "   click_referrer_type  rank  click_cnts  category_id  created_at_ts  \\\n",
       "0                    1    11          11          281  1506942089000   \n",
       "1                    1    10          11            4  1506994257000   \n",
       "2                    1     9          11           99  1507013614000   \n",
       "3                    5    40          40          281  1506983935000   \n",
       "4                    5    39          40          209  1506938444000   \n",
       "\n",
       "   words_count  \n",
       "0          173  \n",
       "1          118  \n",
       "2          213  \n",
       "3          201  \n",
       "4          185  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trn_click = trn_click.merge(item_df, how='left', on=['click_article_id'])\n",
    "trn_click.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train_click_log.csv文件数据中每个字段的含义\n",
    "\n",
    "user_id: 用户的唯一标识  \n",
    "click_article_id: 用户点击的文章唯一标识  \n",
    "click_timestamp: 用户点击文章时的时间戳   \n",
    "click_environment: 用户点击文章的环境  \n",
    "click_deviceGroup: 用户点击文章的设备组  \n",
    "click_os: 用户点击文章时的操作系统  \n",
    "click_country: 用户点击文章时的所在的国家  \n",
    "click_region: 用户点击文章时所在的区域  \n",
    "click_referrer_type: 用户点击文章时，文章的来源  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1112623 entries, 0 to 1112622\n",
      "Data columns (total 14 columns):\n",
      "user_id                1112623 non-null int64\n",
      "click_article_id       1112623 non-null int64\n",
      "click_timestamp        1112623 non-null int64\n",
      "click_environment      1112623 non-null int64\n",
      "click_deviceGroup      1112623 non-null int64\n",
      "click_os               1112623 non-null int64\n",
      "click_country          1112623 non-null int64\n",
      "click_region           1112623 non-null int64\n",
      "click_referrer_type    1112623 non-null int64\n",
      "rank                   1112623 non-null int64\n",
      "click_cnts             1112623 non-null int64\n",
      "category_id            1112623 non-null int64\n",
      "created_at_ts          1112623 non-null int64\n",
      "words_count            1112623 non-null int64\n",
      "dtypes: int64(14)\n",
      "memory usage: 127.3 MB\n"
     ]
    }
   ],
   "source": [
    "#用户点击日志信息\n",
    "trn_click.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>click_environment</th>\n",
       "      <th>click_deviceGroup</th>\n",
       "      <th>click_os</th>\n",
       "      <th>click_country</th>\n",
       "      <th>click_region</th>\n",
       "      <th>click_referrer_type</th>\n",
       "      <th>rank</th>\n",
       "      <th>click_cnts</th>\n",
       "      <th>category_id</th>\n",
       "      <th>created_at_ts</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "      <td>1.112623e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.221198e+05</td>\n",
       "      <td>1.951541e+05</td>\n",
       "      <td>1.507588e+12</td>\n",
       "      <td>3.947786e+00</td>\n",
       "      <td>1.815981e+00</td>\n",
       "      <td>1.301976e+01</td>\n",
       "      <td>1.310776e+00</td>\n",
       "      <td>1.813587e+01</td>\n",
       "      <td>1.910063e+00</td>\n",
       "      <td>7.118518e+00</td>\n",
       "      <td>1.323704e+01</td>\n",
       "      <td>3.056176e+02</td>\n",
       "      <td>1.506598e+12</td>\n",
       "      <td>2.011981e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.540349e+04</td>\n",
       "      <td>9.292286e+04</td>\n",
       "      <td>3.363466e+08</td>\n",
       "      <td>3.276715e-01</td>\n",
       "      <td>1.035170e+00</td>\n",
       "      <td>6.967844e+00</td>\n",
       "      <td>1.618264e+00</td>\n",
       "      <td>7.105832e+00</td>\n",
       "      <td>1.220012e+00</td>\n",
       "      <td>1.016095e+01</td>\n",
       "      <td>1.631503e+01</td>\n",
       "      <td>1.155791e+02</td>\n",
       "      <td>8.343066e+09</td>\n",
       "      <td>5.223881e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.507030e+12</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.166573e+12</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.934700e+04</td>\n",
       "      <td>1.239090e+05</td>\n",
       "      <td>1.507297e+12</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>1.507220e+12</td>\n",
       "      <td>1.700000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.309670e+05</td>\n",
       "      <td>2.038900e+05</td>\n",
       "      <td>1.507596e+12</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>3.280000e+02</td>\n",
       "      <td>1.507553e+12</td>\n",
       "      <td>1.970000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.704010e+05</td>\n",
       "      <td>2.777120e+05</td>\n",
       "      <td>1.507841e+12</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>4.100000e+02</td>\n",
       "      <td>1.507756e+12</td>\n",
       "      <td>2.280000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.999990e+05</td>\n",
       "      <td>3.640460e+05</td>\n",
       "      <td>1.510603e+12</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.410000e+02</td>\n",
       "      <td>2.410000e+02</td>\n",
       "      <td>4.600000e+02</td>\n",
       "      <td>1.510666e+12</td>\n",
       "      <td>6.690000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id  click_article_id  click_timestamp  click_environment  \\\n",
       "count  1.112623e+06      1.112623e+06     1.112623e+06       1.112623e+06   \n",
       "mean   1.221198e+05      1.951541e+05     1.507588e+12       3.947786e+00   \n",
       "std    5.540349e+04      9.292286e+04     3.363466e+08       3.276715e-01   \n",
       "min    0.000000e+00      3.000000e+00     1.507030e+12       1.000000e+00   \n",
       "25%    7.934700e+04      1.239090e+05     1.507297e+12       4.000000e+00   \n",
       "50%    1.309670e+05      2.038900e+05     1.507596e+12       4.000000e+00   \n",
       "75%    1.704010e+05      2.777120e+05     1.507841e+12       4.000000e+00   \n",
       "max    1.999990e+05      3.640460e+05     1.510603e+12       4.000000e+00   \n",
       "\n",
       "       click_deviceGroup      click_os  click_country  click_region  \\\n",
       "count       1.112623e+06  1.112623e+06   1.112623e+06  1.112623e+06   \n",
       "mean        1.815981e+00  1.301976e+01   1.310776e+00  1.813587e+01   \n",
       "std         1.035170e+00  6.967844e+00   1.618264e+00  7.105832e+00   \n",
       "min         1.000000e+00  2.000000e+00   1.000000e+00  1.000000e+00   \n",
       "25%         1.000000e+00  2.000000e+00   1.000000e+00  1.300000e+01   \n",
       "50%         1.000000e+00  1.700000e+01   1.000000e+00  2.100000e+01   \n",
       "75%         3.000000e+00  1.700000e+01   1.000000e+00  2.500000e+01   \n",
       "max         5.000000e+00  2.000000e+01   1.100000e+01  2.800000e+01   \n",
       "\n",
       "       click_referrer_type          rank    click_cnts   category_id  \\\n",
       "count         1.112623e+06  1.112623e+06  1.112623e+06  1.112623e+06   \n",
       "mean          1.910063e+00  7.118518e+00  1.323704e+01  3.056176e+02   \n",
       "std           1.220012e+00  1.016095e+01  1.631503e+01  1.155791e+02   \n",
       "min           1.000000e+00  1.000000e+00  2.000000e+00  1.000000e+00   \n",
       "25%           1.000000e+00  2.000000e+00  4.000000e+00  2.500000e+02   \n",
       "50%           2.000000e+00  4.000000e+00  8.000000e+00  3.280000e+02   \n",
       "75%           2.000000e+00  8.000000e+00  1.600000e+01  4.100000e+02   \n",
       "max           7.000000e+00  2.410000e+02  2.410000e+02  4.600000e+02   \n",
       "\n",
       "       created_at_ts   words_count  \n",
       "count   1.112623e+06  1.112623e+06  \n",
       "mean    1.506598e+12  2.011981e+02  \n",
       "std     8.343066e+09  5.223881e+01  \n",
       "min     1.166573e+12  0.000000e+00  \n",
       "25%     1.507220e+12  1.700000e+02  \n",
       "50%     1.507553e+12  1.970000e+02  \n",
       "75%     1.507756e+12  2.280000e+02  \n",
       "max     1.510666e+12  6.690000e+03  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trn_click.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练集中的用户数量为20w\n",
    "trn_click.user_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trn_click.groupby('user_id')['click_article_id'].count().min()  # 训练集里面每个用户至少点击了两篇文章"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 画直方图大体看一下基本的属性分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['SimHei'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['SimHei'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.figure(figsize=(15, 20))\n",
    "i = 1\n",
    "for col in ['click_article_id', 'click_timestamp', 'click_environment', 'click_deviceGroup', 'click_os', 'click_country', \n",
    "            'click_region', 'click_referrer_type', 'rank', 'click_cnts']:\n",
    "    plot_envs = plt.subplot(5, 2, i)\n",
    "    i += 1\n",
    "    v = trn_click[col].value_counts().reset_index()[:10]\n",
    "    fig = sns.barplot(x=v['index'], y=v[col])\n",
    "    for item in fig.get_xticklabels():\n",
    "        item.set_rotation(90)\n",
    "    plt.title(col)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从点击时间clik_timestamp来看，分布较为平均，可不做特殊处理。由于时间戳是13位的，后续将时间格式转换成10位方便计算。\n",
    "\n",
    "从点击环境click_environment来看，仅有1922次（占0.1%）点击环境为1；仅有24617次（占2.3%）点击环境为2；剩余（占97.6%）点击环境为4。\n",
    "\n",
    "从点击设备组click_deviceGroup来看，设备1占大部分（60.4%），设备3占36%。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 测试集用户点击日志"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>click_environment</th>\n",
       "      <th>click_deviceGroup</th>\n",
       "      <th>click_os</th>\n",
       "      <th>click_country</th>\n",
       "      <th>click_region</th>\n",
       "      <th>click_referrer_type</th>\n",
       "      <th>rank</th>\n",
       "      <th>click_cnts</th>\n",
       "      <th>category_id</th>\n",
       "      <th>created_at_ts</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>249999</td>\n",
       "      <td>160974</td>\n",
       "      <td>1506959142820</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>281</td>\n",
       "      <td>1506912747000</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>249999</td>\n",
       "      <td>160417</td>\n",
       "      <td>1506959172820</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>281</td>\n",
       "      <td>1506942089000</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>249998</td>\n",
       "      <td>160974</td>\n",
       "      <td>1506959056066</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>281</td>\n",
       "      <td>1506912747000</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>249998</td>\n",
       "      <td>202557</td>\n",
       "      <td>1506959086066</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>327</td>\n",
       "      <td>1506938401000</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>249997</td>\n",
       "      <td>183665</td>\n",
       "      <td>1506959088613</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>301</td>\n",
       "      <td>1500895686000</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  click_article_id  click_timestamp  click_environment  \\\n",
       "0   249999            160974    1506959142820                  4   \n",
       "1   249999            160417    1506959172820                  4   \n",
       "2   249998            160974    1506959056066                  4   \n",
       "3   249998            202557    1506959086066                  4   \n",
       "4   249997            183665    1506959088613                  4   \n",
       "\n",
       "   click_deviceGroup  click_os  click_country  click_region  \\\n",
       "0                  1        17              1            13   \n",
       "1                  1        17              1            13   \n",
       "2                  1        12              1            13   \n",
       "3                  1        12              1            13   \n",
       "4                  1        17              1            15   \n",
       "\n",
       "   click_referrer_type  rank  click_cnts  category_id  created_at_ts  \\\n",
       "0                    2    19          19          281  1506912747000   \n",
       "1                    2    18          19          281  1506942089000   \n",
       "2                    2     5           5          281  1506912747000   \n",
       "3                    2     4           5          327  1506938401000   \n",
       "4                    5     7           7          301  1500895686000   \n",
       "\n",
       "   words_count  \n",
       "0          259  \n",
       "1          173  \n",
       "2          259  \n",
       "3          219  \n",
       "4          256  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tst_click = tst_click.merge(item_df, how='left', on=['click_article_id'])\n",
    "tst_click.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>click_environment</th>\n",
       "      <th>click_deviceGroup</th>\n",
       "      <th>click_os</th>\n",
       "      <th>click_country</th>\n",
       "      <th>click_region</th>\n",
       "      <th>click_referrer_type</th>\n",
       "      <th>rank</th>\n",
       "      <th>click_cnts</th>\n",
       "      <th>category_id</th>\n",
       "      <th>created_at_ts</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>518010.000000</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>5.180100e+05</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>518010.000000</td>\n",
       "      <td>5.180100e+05</td>\n",
       "      <td>518010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>227342.428169</td>\n",
       "      <td>193803.792550</td>\n",
       "      <td>1.507387e+12</td>\n",
       "      <td>3.947300</td>\n",
       "      <td>1.738285</td>\n",
       "      <td>13.628467</td>\n",
       "      <td>1.348209</td>\n",
       "      <td>18.250250</td>\n",
       "      <td>1.819614</td>\n",
       "      <td>15.521785</td>\n",
       "      <td>30.043586</td>\n",
       "      <td>305.324961</td>\n",
       "      <td>1.506883e+12</td>\n",
       "      <td>210.966331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14613.907188</td>\n",
       "      <td>88279.388177</td>\n",
       "      <td>3.706127e+08</td>\n",
       "      <td>0.323916</td>\n",
       "      <td>1.020858</td>\n",
       "      <td>6.625564</td>\n",
       "      <td>1.703524</td>\n",
       "      <td>7.060798</td>\n",
       "      <td>1.082657</td>\n",
       "      <td>33.957702</td>\n",
       "      <td>56.868021</td>\n",
       "      <td>110.411513</td>\n",
       "      <td>5.816668e+09</td>\n",
       "      <td>83.040065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>1.506959e+12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.265812e+12</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>214926.000000</td>\n",
       "      <td>128551.000000</td>\n",
       "      <td>1.507026e+12</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>1.506970e+12</td>\n",
       "      <td>176.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>229109.000000</td>\n",
       "      <td>199197.000000</td>\n",
       "      <td>1.507308e+12</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>323.000000</td>\n",
       "      <td>1.507249e+12</td>\n",
       "      <td>199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>240182.000000</td>\n",
       "      <td>272143.000000</td>\n",
       "      <td>1.507666e+12</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>1.507630e+12</td>\n",
       "      <td>232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>249999.000000</td>\n",
       "      <td>364043.000000</td>\n",
       "      <td>1.508832e+12</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>938.000000</td>\n",
       "      <td>938.000000</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>1.509949e+12</td>\n",
       "      <td>3082.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id  click_article_id  click_timestamp  click_environment  \\\n",
       "count  518010.000000     518010.000000     5.180100e+05      518010.000000   \n",
       "mean   227342.428169     193803.792550     1.507387e+12           3.947300   \n",
       "std     14613.907188      88279.388177     3.706127e+08           0.323916   \n",
       "min    200000.000000        137.000000     1.506959e+12           1.000000   \n",
       "25%    214926.000000     128551.000000     1.507026e+12           4.000000   \n",
       "50%    229109.000000     199197.000000     1.507308e+12           4.000000   \n",
       "75%    240182.000000     272143.000000     1.507666e+12           4.000000   \n",
       "max    249999.000000     364043.000000     1.508832e+12           4.000000   \n",
       "\n",
       "       click_deviceGroup       click_os  click_country   click_region  \\\n",
       "count      518010.000000  518010.000000  518010.000000  518010.000000   \n",
       "mean            1.738285      13.628467       1.348209      18.250250   \n",
       "std             1.020858       6.625564       1.703524       7.060798   \n",
       "min             1.000000       2.000000       1.000000       1.000000   \n",
       "25%             1.000000      12.000000       1.000000      13.000000   \n",
       "50%             1.000000      17.000000       1.000000      21.000000   \n",
       "75%             3.000000      17.000000       1.000000      25.000000   \n",
       "max             5.000000      20.000000      11.000000      28.000000   \n",
       "\n",
       "       click_referrer_type           rank     click_cnts    category_id  \\\n",
       "count        518010.000000  518010.000000  518010.000000  518010.000000   \n",
       "mean              1.819614      15.521785      30.043586     305.324961   \n",
       "std               1.082657      33.957702      56.868021     110.411513   \n",
       "min               1.000000       1.000000       1.000000       1.000000   \n",
       "25%               1.000000       4.000000      10.000000     252.000000   \n",
       "50%               2.000000       8.000000      19.000000     323.000000   \n",
       "75%               2.000000      18.000000      35.000000     399.000000   \n",
       "max               7.000000     938.000000     938.000000     460.000000   \n",
       "\n",
       "       created_at_ts    words_count  \n",
       "count   5.180100e+05  518010.000000  \n",
       "mean    1.506883e+12     210.966331  \n",
       "std     5.816668e+09      83.040065  \n",
       "min     1.265812e+12       0.000000  \n",
       "25%     1.506970e+12     176.000000  \n",
       "50%     1.507249e+12     199.000000  \n",
       "75%     1.507630e+12     232.000000  \n",
       "max     1.509949e+12    3082.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tst_click.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以看出训练集和测试集的用户是完全不一样的\n",
    "\n",
    "训练集的用户ID由0 ~ 199999，而测试集A的用户ID由200000 ~ 249999。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#测试集中的用户数量为5w\n",
    "tst_click.user_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tst_click.groupby('user_id')['click_article_id'].count().min() # 注意测试集里面有只点击过一次文章的用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>created_at_ts</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1513144419000</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1405341936000</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1408667706000</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1408468313000</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1407071171000</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364042</th>\n",
       "      <td>364042</td>\n",
       "      <td>460</td>\n",
       "      <td>1434034118000</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364043</th>\n",
       "      <td>364043</td>\n",
       "      <td>460</td>\n",
       "      <td>1434148472000</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364044</th>\n",
       "      <td>364044</td>\n",
       "      <td>460</td>\n",
       "      <td>1457974279000</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364045</th>\n",
       "      <td>364045</td>\n",
       "      <td>460</td>\n",
       "      <td>1515964737000</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364046</th>\n",
       "      <td>364046</td>\n",
       "      <td>460</td>\n",
       "      <td>1505811330000</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        click_article_id  category_id  created_at_ts  words_count\n",
       "0                      0            0  1513144419000          168\n",
       "1                      1            1  1405341936000          189\n",
       "2                      2            1  1408667706000          250\n",
       "3                      3            1  1408468313000          230\n",
       "4                      4            1  1407071171000          162\n",
       "364042            364042          460  1434034118000          144\n",
       "364043            364043          460  1434148472000          463\n",
       "364044            364044          460  1457974279000          177\n",
       "364045            364045          460  1515964737000          126\n",
       "364046            364046          460  1505811330000          479"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#新闻文章数据集浏览\n",
    "item_df.head().append(item_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "176     3485\n",
       "182     3480\n",
       "179     3463\n",
       "178     3458\n",
       "174     3456\n",
       "183     3432\n",
       "184     3427\n",
       "173     3414\n",
       "180     3403\n",
       "177     3391\n",
       "170     3387\n",
       "187     3355\n",
       "169     3352\n",
       "185     3348\n",
       "175     3346\n",
       "181     3330\n",
       "186     3328\n",
       "189     3327\n",
       "171     3327\n",
       "172     3322\n",
       "165     3308\n",
       "188     3288\n",
       "167     3269\n",
       "190     3261\n",
       "192     3257\n",
       "168     3248\n",
       "193     3225\n",
       "166     3199\n",
       "191     3182\n",
       "194     3164\n",
       "        ... \n",
       "601        1\n",
       "857        1\n",
       "1977       1\n",
       "1626       1\n",
       "697        1\n",
       "1720       1\n",
       "696        1\n",
       "706        1\n",
       "592        1\n",
       "1605       1\n",
       "586        1\n",
       "582        1\n",
       "1606       1\n",
       "972        1\n",
       "716        1\n",
       "584        1\n",
       "1608       1\n",
       "715        1\n",
       "841        1\n",
       "968        1\n",
       "964        1\n",
       "587        1\n",
       "1099       1\n",
       "1355       1\n",
       "711        1\n",
       "845        1\n",
       "710        1\n",
       "965        1\n",
       "847        1\n",
       "1535       1\n",
       "Name: words_count, Length: 866, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_df['words_count'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "461\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(item_df['category_id'].nunique())     # 461个文章主题\n",
    "item_df['category_id'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(364047, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_df.shape       # 364047篇文章"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 新闻文章embedding向量表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>emb_0</th>\n",
       "      <th>emb_1</th>\n",
       "      <th>emb_2</th>\n",
       "      <th>emb_3</th>\n",
       "      <th>emb_4</th>\n",
       "      <th>emb_5</th>\n",
       "      <th>emb_6</th>\n",
       "      <th>emb_7</th>\n",
       "      <th>emb_8</th>\n",
       "      <th>...</th>\n",
       "      <th>emb_240</th>\n",
       "      <th>emb_241</th>\n",
       "      <th>emb_242</th>\n",
       "      <th>emb_243</th>\n",
       "      <th>emb_244</th>\n",
       "      <th>emb_245</th>\n",
       "      <th>emb_246</th>\n",
       "      <th>emb_247</th>\n",
       "      <th>emb_248</th>\n",
       "      <th>emb_249</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.161183</td>\n",
       "      <td>-0.957233</td>\n",
       "      <td>-0.137944</td>\n",
       "      <td>0.050855</td>\n",
       "      <td>0.830055</td>\n",
       "      <td>0.901365</td>\n",
       "      <td>-0.335148</td>\n",
       "      <td>-0.559561</td>\n",
       "      <td>-0.500603</td>\n",
       "      <td>...</td>\n",
       "      <td>0.321248</td>\n",
       "      <td>0.313999</td>\n",
       "      <td>0.636412</td>\n",
       "      <td>0.169179</td>\n",
       "      <td>0.540524</td>\n",
       "      <td>-0.813182</td>\n",
       "      <td>0.286870</td>\n",
       "      <td>-0.231686</td>\n",
       "      <td>0.597416</td>\n",
       "      <td>0.409623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.523216</td>\n",
       "      <td>-0.974058</td>\n",
       "      <td>0.738608</td>\n",
       "      <td>0.155234</td>\n",
       "      <td>0.626294</td>\n",
       "      <td>0.485297</td>\n",
       "      <td>-0.715657</td>\n",
       "      <td>-0.897996</td>\n",
       "      <td>-0.359747</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.487843</td>\n",
       "      <td>0.823124</td>\n",
       "      <td>0.412688</td>\n",
       "      <td>-0.338654</td>\n",
       "      <td>0.320786</td>\n",
       "      <td>0.588643</td>\n",
       "      <td>-0.594137</td>\n",
       "      <td>0.182828</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>-0.834364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.619619</td>\n",
       "      <td>-0.972960</td>\n",
       "      <td>-0.207360</td>\n",
       "      <td>-0.128861</td>\n",
       "      <td>0.044748</td>\n",
       "      <td>-0.387535</td>\n",
       "      <td>-0.730477</td>\n",
       "      <td>-0.066126</td>\n",
       "      <td>-0.754899</td>\n",
       "      <td>...</td>\n",
       "      <td>0.454756</td>\n",
       "      <td>0.473184</td>\n",
       "      <td>0.377866</td>\n",
       "      <td>-0.863887</td>\n",
       "      <td>-0.383365</td>\n",
       "      <td>0.137721</td>\n",
       "      <td>-0.810877</td>\n",
       "      <td>-0.447580</td>\n",
       "      <td>0.805932</td>\n",
       "      <td>-0.285284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.740843</td>\n",
       "      <td>-0.975749</td>\n",
       "      <td>0.391698</td>\n",
       "      <td>0.641738</td>\n",
       "      <td>-0.268645</td>\n",
       "      <td>0.191745</td>\n",
       "      <td>-0.825593</td>\n",
       "      <td>-0.710591</td>\n",
       "      <td>-0.040099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.271535</td>\n",
       "      <td>0.036040</td>\n",
       "      <td>0.480029</td>\n",
       "      <td>-0.763173</td>\n",
       "      <td>0.022627</td>\n",
       "      <td>0.565165</td>\n",
       "      <td>-0.910286</td>\n",
       "      <td>-0.537838</td>\n",
       "      <td>0.243541</td>\n",
       "      <td>-0.885329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.279052</td>\n",
       "      <td>-0.972315</td>\n",
       "      <td>0.685374</td>\n",
       "      <td>0.113056</td>\n",
       "      <td>0.238315</td>\n",
       "      <td>0.271913</td>\n",
       "      <td>-0.568816</td>\n",
       "      <td>0.341194</td>\n",
       "      <td>-0.600554</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238286</td>\n",
       "      <td>0.809268</td>\n",
       "      <td>0.427521</td>\n",
       "      <td>-0.615932</td>\n",
       "      <td>-0.503697</td>\n",
       "      <td>0.614450</td>\n",
       "      <td>-0.917760</td>\n",
       "      <td>-0.424061</td>\n",
       "      <td>0.185484</td>\n",
       "      <td>-0.580292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 251 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id     emb_0     emb_1     emb_2     emb_3     emb_4     emb_5  \\\n",
       "0           0 -0.161183 -0.957233 -0.137944  0.050855  0.830055  0.901365   \n",
       "1           1 -0.523216 -0.974058  0.738608  0.155234  0.626294  0.485297   \n",
       "2           2 -0.619619 -0.972960 -0.207360 -0.128861  0.044748 -0.387535   \n",
       "3           3 -0.740843 -0.975749  0.391698  0.641738 -0.268645  0.191745   \n",
       "4           4 -0.279052 -0.972315  0.685374  0.113056  0.238315  0.271913   \n",
       "\n",
       "      emb_6     emb_7     emb_8    ...      emb_240   emb_241   emb_242  \\\n",
       "0 -0.335148 -0.559561 -0.500603    ...     0.321248  0.313999  0.636412   \n",
       "1 -0.715657 -0.897996 -0.359747    ...    -0.487843  0.823124  0.412688   \n",
       "2 -0.730477 -0.066126 -0.754899    ...     0.454756  0.473184  0.377866   \n",
       "3 -0.825593 -0.710591 -0.040099    ...     0.271535  0.036040  0.480029   \n",
       "4 -0.568816  0.341194 -0.600554    ...     0.238286  0.809268  0.427521   \n",
       "\n",
       "    emb_243   emb_244   emb_245   emb_246   emb_247   emb_248   emb_249  \n",
       "0  0.169179  0.540524 -0.813182  0.286870 -0.231686  0.597416  0.409623  \n",
       "1 -0.338654  0.320786  0.588643 -0.594137  0.182828  0.397090 -0.834364  \n",
       "2 -0.863887 -0.383365  0.137721 -0.810877 -0.447580  0.805932 -0.285284  \n",
       "3 -0.763173  0.022627  0.565165 -0.910286 -0.537838  0.243541 -0.885329  \n",
       "4 -0.615932 -0.503697  0.614450 -0.917760 -0.424061  0.185484 -0.580292  \n",
       "\n",
       "[5 rows x 251 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_emb_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(364047, 251)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_emb_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>30760</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>157507</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>63746</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>289197</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>36162</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>168401</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>36162</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>50644</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>39894</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>42567</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  click_article_id  count\n",
       "0        0             30760      1\n",
       "1        0            157507      1\n",
       "2        1             63746      1\n",
       "3        1            289197      1\n",
       "4        2             36162      1\n",
       "5        2            168401      1\n",
       "6        3             36162      1\n",
       "7        3             50644      1\n",
       "8        4             39894      1\n",
       "9        4             42567      1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#####merge\n",
    "user_click_merge = trn_click.append(tst_click)\n",
    "#用户重复点击\n",
    "user_click_count = user_click_merge.groupby(['user_id', 'click_article_id'])['click_timestamp'].agg({'count'}).reset_index()\n",
    "user_click_count[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>311242</th>\n",
       "      <td>86295</td>\n",
       "      <td>74254</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311243</th>\n",
       "      <td>86295</td>\n",
       "      <td>76268</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393761</th>\n",
       "      <td>103237</td>\n",
       "      <td>205948</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393763</th>\n",
       "      <td>103237</td>\n",
       "      <td>235689</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576902</th>\n",
       "      <td>134850</td>\n",
       "      <td>69463</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  click_article_id  count\n",
       "311242    86295             74254     10\n",
       "311243    86295             76268     10\n",
       "393761   103237            205948     10\n",
       "393763   103237            235689     10\n",
       "576902   134850             69463     13"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_click_count[user_click_count['count']>7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  4,  3,  6,  5, 10,  7, 13])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_click_count['count'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     1605541\n",
       "2       11621\n",
       "3         422\n",
       "4          77\n",
       "5          26\n",
       "6          12\n",
       "10          4\n",
       "7           3\n",
       "13          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户点击新闻次数\n",
    "user_click_count.loc[:,'count'].value_counts() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出：有1605541（约占99.2%）的用户未重复阅读过文章，仅有极少数用户重复点击过某篇文章。 这个也可以单独制作成特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用户点击环境变化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_envs(df, cols, r, c):\n",
    "    plt.figure()\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    i = 1\n",
    "    for col in cols:\n",
    "        plt.subplot(r, c, i)\n",
    "        i += 1\n",
    "        v = df[col].value_counts().reset_index()\n",
    "        fig = sns.barplot(x=v['index'], y=v[col])\n",
    "        for item in fig.get_xticklabels():\n",
    "            item.set_rotation(90)\n",
    "        plt.title(col)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vtjs97vE8YIbtRZJ2AP4X2KjZhraPAY4BmDVrljscR8Ryw/YTkr5he3OKHOuk5GzE4Ei+RoxROzfpHSLpu5L2krTr0DSek9t+wPai8vXZwGRJU8ZzzIgA4FxJu0lqpd9xy5KzEYMj+Roxdu1cQd4X2ASYzFNdLAycOdaTS3o2xQMMLOllFAX73WM9XkQ86X3Ah4HFkh6luEHPtp8+noMmZyMGR/I1YuzaKZBfanvjdg4u6RRga2CKpIXAIRQFNra/BbwF+A9Ji4FHgD1t56udiHGyvcZY9kvORgyO5GtE97RTIF8iaVPb17W6g+29Rln/dYohaiKiAyRNAlYZ+lpV0suBFcvVf7D94Ej7J2cjBkfyNaJ72imQXw7Ml3QzxcNChr6yHXaYt4jouS8BdwJfLudPAa4BVqa4YedjNcUVERExMNopkLfrWhQR0SmvBV5amb/P9k7lzXoX1hRTRETEQGl5FAvbtwILgccpbs4bmiKif6xge3Fl/mNQfNUDrF5PSBHRKknPkJRvZiNq1s6T9D5IcQPA31h6FIskckT/WFHSGkN9jW3/CkDSmhTdLCKiz0g6H9iZok2+ArhT0sW2P1xrYBETWDvjIO8PbGz7BbZfVE4pjiP6y3eA0yQ9+SgsSTMo+iJ/t7aoImIka9p+ANgVOMn2VsC2NccUMaG10wd5AXB/twKJiPGzfaSkh4GLJK1GcTPtg8AXbX+z3ugiYhhPk/Qc4K3AJ+sOJiLaK5BvAs6XdBbFKBZA0SB3PKqIGLNy/NNvSVqjnB9xaLeIqN1hwC+Bi21fLum5wJ9qjiliQmunQP5LOa3IU+OqRkQfkvQs4PPAusD2kjYFXmH72Hoji4hGtk8HTq/M3wTsVl9EEdFygWz7MwCSVi/nF3UrqIgYtxOA43nq69o/AqcBKZAj+oyk9YGvAa8qF10I7G97YX1RRUxsLd+kJ+mFkv4AXAtcK+kKSS/oXmgRMQ5TbP+QcsSZcui3JfWGFBHDOB6YQ/GNz7rAT8tlEVGTdkaxOAb4sO0ZtmcAH6G4Yz4i+s9DktahHKu8fOR0brKN6E9TbR9ve3E5nQBMrTuoiImsnT7Iq9n+zdCM7fPLu+Qjov98mOKK1IaSLqZobN9Sb0gRMYy7Jb2dYjhGgL2Au2uMJ2LCa2sUC0n/D/heOf92ipEtIqLP2J4n6TXAxhRDvd1g+/Gaw4qI5t5F0Qf5qxTf+lwC7FtrRBETXDtdLN5FcRXqTOBHwJRyWUT0GUkfAFa3fa3ta4DVJb2/7rgiYlm2b7W9s+2ptp9p+022/zK0XtLBdcYXMRG1VCBLmgScaftDtrewvaXtA2zf2+X4ImJs3mv7vqGZMlffW184ETEOu9cdQMRE01KBbHsJ8ISkNbscT0R0xiRJGpopP+Rm/PKIwaTRN4mITmqnD/Ii4GpJ5wAPDS20/aGORxUR4/UL4DRJ3y7n31cui4jB47oDiJho2imQzyyniOh/H6Moiv+jnD8H+G594UTEOOQKckSPtfMkvRPbPbik44AdgTttv7DJegFHATsADwP72J7X7nkiYmm2nwC+WU4tS85G9J6ktW3f07BsA9s3l7OnN9kt+RrRRaP2QZb0w/Ln1ZKuapxG2f0EYLsR1m8PbFROs2mzMY+IpY0zXyE5G1GHn0p6+tCMpE0pnqYHgO3PD7PfCSRfI7qilSvI+5c/d2z34LYvkDRzhE12AU6ybeD3ktaS9Bzbd7R7rogAxpGvkJyNqMnnKYrkN1KMXX4SsPdoOyVfI7pn1AK5kki7Aafavr2D518PWFCZX1guWyZ5Jc2m+ATM9OnTOxhCxPKjy/kKydmIjrN9lqTJwK+ANYA32/5jBw6dfI0Yo3YeFLIGcI6kCyXtJ+lZ3QqqGdvH2J5le9bUqXlEfcQoas1XSM5GjEbS1yQdLeloYBtgTeBmYL9yWc8kXyOW1s5Nep8BPiNpM2AP4LeSFtredhznvw2YVplfv1wWEePQpXyF5GxEJ81tmL+iw8dPvkaMUTvDvA25E/grcDfwzHGefw7FJ+VTga2A+9M3KqKjOpmvkJyN6Jih0aEkrQY8Wj6Ua+jBPit14BTJ14gxarlAlvR+4K3AVIohZ95r+7pR9jkF2BqYImkhcAgwGcD2t4CzKYafuZFiCJp9238LEdFoLPla7pecjei9c4FtKR7IBbAKRX/kV460U/I1onvauYI8DTjA9vxWd7C91yjrDXygjRgiojVt5yskZyNqsrLtoeIY24skrTraTsnXiO5p+SY92wdTPGp6XUnTh6YuxhYRY1Tm6+qS9gWQNFXSBjWHFRHNPSRpi6EZSVsCj9QYT8SE104Xi/2AQ4G/AU+Uiw1s1vmwImI8JB0CzKIYU/V4iq9dvw+8qs64IqKpA4DTJd1O8VjpZ1PcXBsRNWmni8UBwMa27+5SLBHROW8GNgfmAdi+XdIa9YYUEc3YvlzSJhQfaAFusP14nTFFTHTtFMgLgPu7FUhEdNQ/bFuS4cm75COij0jaxvZ5knZtWPV8Sdg+s5bAIqKtAvkm4HxJZwGPDS20fWTHo4qI8fqhpG8Da0l6L/Au4Ds1xxQRS3sNcB6wU5N1BlIgR9SknQL5L+W0YjlFRJ+y/RVJrwMeoPja9tO2z6k5rIiosH1I+TPDr0X0mXafpIekVW0/3L2QIqITyoI4RXFEn5L04ZHW5xvaiPq0M4rFK4BjgdWB6ZJeDLzP9vu7FVxEtEfSgxRfzTZl++k9DCciRjbSjbPD5nFEdF87XSz+G3gDxaMrsX2lpH/pRlARMTa21wCQdDhwB/A9imGj9gaeU2NoEdGg8s3sicD+tu8r558BHFFjaBETXssPCgGwvaBh0ZIOxhIRnbOz7f+x/aDtB2x/E9il7qAioqnNhopjANv3UgzTGBE1aadAXiDplYAlTZZ0IHB9l+KKiPF5SNLekiZJWkHS3sBDdQcVEU2tUF41BkDS2rT3DW9EdFg7CfjvwFHAesBtwK/IM94j+tXbKPL1KIq+jBeXyyKi/xwB/E7S6eX87sDnaownYsJrZxSLuyj6MTYl6WDbX+hIVBExLrZvYYQuFcnXiP5h+yRJc4FtykW72r6uzpgiJrq2+iCPYvcOHisiuiv5GtFHbF9n++vllOI4omadLJDVwWNFRHclXyMiIobRyQI5YzZGDI7ka0RExDByBTliYkq+RkREDKPlArkcdqZx2QaV2dMb10dEPZKvERERY9fOFeSfSnryMbWSNgV+OjRv+/PNdpK0naQbJN0o6eNN1u8j6e+S5pfTe9p5AxHRVPI1YgJIzkZ0RzvjIH+eotF9I7AxcBIjDPsGIGkS8A3gdcBC4HJJc5rcoXua7f3aiCUiRpZ8jVjOJWcjuqedcZDPkjSZ4gEhawBvtv3HUXZ7GXCj7ZsAJJ1KMTZrhrCJ6KLka8SEkJyN6JJRC2RJX2PpO97XBP4M7CcJ2x8aYff1gAWV+YXAVk22203SvwB/BP7T9oLGDSTNBmYDTJ8+fbSwIyakfsnXMpbkbER3pY2N6JJWriDPbZi/osMx/BQ4xfZjkt4HnMhTTxN6ku1jgGMAZs2alSGqIprri3yF5GxEn0gbGzEGoxbItk8EkLQa8KjtJeX8JGClUXa/DZhWmV+/XFY9/t2V2e8CXx497IhoJvkaMaEkZyO6pJ1RLM4FVqnMrwL8epR9Lgc2krSBpBWBPYE51Q0kPacyuzNwfRsxRURzydeI5V9yNqJL2hnFYmXbi4ZmbC+StOpIO9heLGk/4JfAJOA429dKOgyYa3sO8CFJOwOLgXuAfdp9ExGxjORrxHIuORvRPe0UyA9J2sL2PABJWwKPjLaT7bOBsxuWfbry+mDg4DbiiIjRJV8jJoDkbER3tFMgHwCcLul2isfUPhvYoxtBRcS4HUDyNSIiYkzaGQf5ckmbUDx0AOAG2493J6yIGI/ka0RExNi1Mg7yNrbPk7Rrw6rnl+Oqntml2CKiTcnXiIiI8WvlCvJrgPOAnZqsM5AGN6J/JF8jIiLGqZVxkA8pf+7b/XAiYjySrxEREePXSheLD4+03vaRnQsnIsYj+RoRETF+rXSxWGOEdXkcZUR/Sb5GRESMUytdLD4DIOlEYH/b95XzzwCO6Gp0EdGW5GtERMT4tfOo6c2GGlsA2/cCm3c8oojohORrRETEGLVTIK9QXoUCQNLatPegkYjoneRrRETEGLXTYB4B/E7S6eX87sDnOh9SRHRA8jUiImKM2nmS3kmS5gLblIt2tX1dd8KKiPFIvkZERIxdW1+5lg1sGtmIAZB8jYiIGJt2+iBHRERERCz3UiBHRERERFSkQI6IiIiIqEiBHBERERFRkQI5IiIiIqKi6wWypO0k3SDpRkkfb7J+JUmnlesvlTSz2zFFRHPJ14jBkpyN6I6uFsiSJgHfALYHNgX2krRpw2bvBu61/Tzgq8CXuhlTRDSXfI0YLMnZiO7p9hXklwE32r7J9j+AU4FdGrbZBTixfH0G8FpJ6nJcEbGs5GvEYEnORnRJtwvk9YAFlfmF5bKm29heDNwPrNPluCJiWcnXiMGSnI3okraepFcnSbOB2eXsIkk31BnPBDUFuKvuIAaBvvJvnTzcjE4erFeSs7VLvrYo+Zp87RPJ2Rb1Ime7XSDfBkyrzK9fLmu2zUJJTwPWBO5uPJDtY4BjuhRntEDSXNuz6o4juqZj+QrJ2bolXyeEtLHLkeRsf+l2F4vLgY0kbSBpRWBPYE7DNnOAoY8CbwHOs+0uxxURy0q+RgyW5GxEl3T1CrLtxZL2A34JTAKOs32tpMOAubbnAMcC35N0I3APRYJHRI8lXyMGS3I2onuUD5LRKkmzy6/hIqLPJV8jBktytr+kQI6IiIiIqMijpiMiIiIiKlIgR0RERERUpECOiIiIiKhIgRwtkXRS3TFERERE9MLAPEkvekdS4ziaAv5V0loAtnfueVARMSaS9rV9fN1xREQMkoxiEcuQNA+4DvguYIoC+RTK8TNt/7a+6CKiHZL+Ynt63XFExFPKdvZM4BTbf647nlhWriBHM7OA/YFPAgfZni/pkRTGEf1J0lXDrQKe1ctYIqIlzwDWAn4j6a8UF6FOs317rVHFk3IFOYYlaX3gq8DfgJ1zFSqiP0n6G/AG4N7GVcAlttftfVQRMRxJ82xvUb7+Z2AvYFfgeoqrynlgSM1yBTmGZXshsLukNwIP1B1PRAzrZ8Dqtuc3rpB0fs+jiYiW2b4QuFDSB4HXAXsAKZBrlivIERERET0k6VTbe9YdRwwvw7xFRERE9NBIxbGkfXsZSzSXK8gRERERfSIjz/SHXEGeoCRd0ub2W0v6WbfiiYiRJWcjlh+SrhpmupqMPNMXcpPeBGX7lXXHEBGtS85GLFeexQgjz/Q+nGiUK8gTlKRF5c+tJZ0v6QxJ/yfpZEkq121XLptHMfzM0L6rSTpO0mWS/iBpl3L5UZI+Xb5+g6QLJOX/WEQHJGcjlitDI8/c2jDdApxfb2gBuYIchc2BFwC3AxcDr5I0F/gOsA1wI3BaZftPAufZflf5+OnLJP0aOBi4XNKFwNHADraf6N3biJgwkrMRA8z2u0dY97ZexhLN5UpBAFxme2HZMM4HZgKbADfb/pOLOzm/X9n+9cDHJc2n+KS7MjDd9sPAe4FzgK/n8ZkRXZOcjYjoolxBDoDHKq+XMPr/CwG72b6hyboXAXcDeXJXRPckZyMiuihXkGM4/wfMlLRhOb9XZd0vgQ9W+j1uXv6cAXyE4uvf7SVt1cN4Iya65GxEn8ioM4MvBXI0ZftRYDZwVnnDz52V1YcDk4GrJF0LHF42vMcCB9q+HXg38F1JK/c49IgJKTkb0T8y6szgy4NCIiIiIjpI0iLbq0vaGjgUuAt4IXAF8HbblrQd8N/Aw8BFwHNt7yhpNeBr5faTgUNt/0TSUcDdtg+T9AaKm2+3zo213ZE+yBERERHdk1FnBlC6WERERER0T0adGUC5ghwRERHRPRl1ZgDlCnJEREREb2XUmT6XAjkiIiKihzLqTP/LKBYRERERERW5ghwRERERUZECOSIiIiKiIgVyRERERERFCuSIiIiIiIoUyBERERERFSmQIyIiIiIqUiBHRERERFSkQI6IiIiIqEiBHBERERFRkQI5IiIiIqIiBXJEREREREUK5IiIiIiIihTIA0LSPpIuqswvkvTcUfaZKcmSntb9CCNiJIOaw5L2lvSrus4f0Yp+zi9Jr5L0pzKmN3XzXNE5KZwGlO3V646hUyTNBG4GJtteXHM4ET0xKDls+2Tg5LrjiGhHn+XXYcDXbR9VdyCNJB0KPM/22+uOpd/kCnIMhFwFjxib5E5E97SYXzOAazt1fEmTxnuMGF0K5D4kaZqkMyX9XdLdkr7eZBtLel75ehVJR0i6VdL9ki6StEqTfXaTdIukF45y/ldLukTSfZIWSNqnXL6mpJPKuG6V9ClJK5TrDpX0/coxlvrqStL5kg6XdLGkByX9StKUcvMLyp/3lV9BvaL8uuxiSV+VdDdwmKR7JL2oco5nSnpY0tR2fr8R3VZnDldy792S/gKcVy5/l6TrJd0r6ZeSZlT2eb2kG8pz/4+k30p6T7mu8avrV0q6vNz2ckmvrKwbKc8jOmKQ8kvSn4HnAj8t27eVyrb0WEl3SLpN0mdVFr1N2r5DJZ0g6ZuSzpb0EPCvktaV9KPyd3CzpA9VYjxU0hmSvi/pAWCfYd7LdsAngD3K2K6UtLukKxq2+7Ckn5SvT5D0LUnnlDn+24a/JZuU6+4p/6a8dbjfZb9LgdxnyiT5GXArMBNYDzh1lN2+AmwJvBJYG/go8ETDcfcFvgRsa/uaEc4/A/g58DVgKvASYH65+mvAmhTJ/hrgncC+Lb41gLeV2z8TWBE4sFz+L+XPtWyvbvt35fxWwE3As4DDKX4P1a+B9gLOtf33NmKI6Kq6c7jiNcA/AW+QtAtFQ7grRV5fCJxSHncKcAZwMLAOcEMZR7P3tjZwFnB0ue2RwFmS1qlsNlyeR4zboOWX7Q2BvwA7le3bY8AJwGLgecDmwOuB91SOXW37Plcue1v5eg3gEuCnwJXl+38tcICkN1SOsQtFXq/FMF2kbP8C+DxwWhnbi4E5wAaS/qmy6TuAkyrze1O0yVMo6oOTASStBpwD/IAi//cE/kfSpk1/g/3OdqY+moBXAH8HntawfB/gosq8KZJrBeAR4MVNjjWz3O5A4Dpg/RbOfzDw4ybLJwH/ADatLHsfcH75+lDg+03O/bRy/nzgU5X17wd+0Wzbyvv9S0MMW1H8oVE5Pxd4a93/ZpkyVac+yOGhfZ5bWfZz4N2V+RWAhym++n0n8LvKOgELgPc0xk3RUF7WcL7fAfuUr4fN80yZOjENWn6V87dQFN5QFL2PAatUtt8L+E3lfTS2fScAJ1Xmt2qyzcHA8eXrQ4ELWvx9Hkql7S6XfRP4XPn6BcC9wEqVWE6tbLs6sASYBuwBXNhwrG8Dh9T9/2YsU64g959pwK1u/Wa1KcDKwJ9H2OYg4Bu2F7Z4/mbHmgJMpvjUPuRWik+vrfpr5fXDFIk1kgXVGduXlvttLWkTij9+c9o4f0Qv1J3DQ6r5MwM4SkW3qfuAeygK4fWAdavbumjVhjvPuiz9NwCW/TvQbp5HtGPQ8qvRDIq29I7K9t+muOLa7NjDnW/dof3LY3yCovge6RitOhF4myRRfCj+oYsr38sc2/Yiive7bhnXVg1x7Q08exyx1CYdt/vPAmC6pKe1+AfgLuBRYEOKr1uaeT3wC0l/tf2jFs7/smHO8zhFAlxXLpsO3Fa+fghYtbJ9OwnhNpafSNHN4q/AGbYfbeM8Eb1Qdw4PqebPAoorQst81SppI2D9yryq8w1up/gbUDUd+EWLMUWM10DlVxMLKK4gTxkh/mZtX+P5bra9UYvxjWSZ7Wz/XtI/gH+m6NrxtoZNpg29kLQ6RbeV28u4fmv7dS2eu6/lCnL/uQy4A/iipNUkrSzpVcNtbPsJ4DjgyLLT/iQVN7mtVNnsWmA74BuSdh7l/CcD20p6q6SnSVpH0ktsLwF+CHxO0hplX+UPA0M35s0H/kXSdElrUnzd06q/U/QHG3HMytL3gTdTFMknjbJtRB3qzuFmvgUcLOkF8OQNt7uX684CXiTpTSpuqv0Aw3/APRt4vqS3lX8f9gA2pegTGtELg5ZfjfHcAfwKOELS0yWtIGlDSa9p43yXAQ9K+piKGxAnSXqhpJeOIfa/ATNV3nBfcRLwdeBx2xc1rNtBxc38K1L0Rf697QUUfweeL+kdkiaX00sb+jMPjBTIfaYsRHei6D7wF4qvOvcYZbcDgauByym+6vgSDf+2tq8EdgS+I2n7Ec7/F2AH4CPlseYDLy5Xf5DiSvFNwEUUHfGPK/c7BzgNuAq4gjYaTNsPU9x8cHH5tczLR9h2ATCP4lPvha2eI6JX6s7hYWL6cXnMU1Xc1X4NsH257i5gd+DLwN0UBe9ciqtcjce5u4zhI+W2HwV2LI8R0XWDll/DeCfFDazXUfTvPQN4ThvnW1LG+hKKZwjcBXyX4ib6dp1e/rxb0rzK8u8BL+Spi2BVPwAOofhdbkl587ztBymuxu9JcUX5rxS/l5WaHKPvDd3sFDEwJB0H3G77U3XHErG8Ka8kLQT2tv2buuOJiN5TMQzencAWtv9UWX4CsHAitL/pgxwDRcVT93alGBonIjqgHB7qUoq7/Q+iuMHo97UGFRF1+g/g8mpxPNGki8UEJGlvFYOCN05jetJPr0g6nOKrq/+yfXPd8UTUpQs5/AqKu/zvovj6+k22H+lYwBEDZFDbyOFI+vkw7+cTw2x/C7A/RVeqCStdLCIiIiIiKnIFOSIiIiKiYiD7IE+ZMsUzZ86sO4yInrjiiivusj217jjGIzkbE0XyNWKwDJezA1kgz5w5k7lz59YdRkRPSGp8ctnASc7GRJF8jRgsw+VsV7tYSJom6TeSrpN0raT9m2wjSUdLulHSVZK26GZMEdFc8jVisCRnI7qn21eQFwMfsT1P0hrAFZLOsX1dZZvtgY3KaSvgm+XPiOit5GvEYEnORnRJV68g277D9rzy9YPA9cB6DZvtApzkwu+BtSS1/ESZiOiM5GvEYEnORnRPz/oglw942JxiMPqq9YAFlfmF5bI7GvafDcwGmD59esfi2vKgkzp2rIghV/zXO+sOYVzGm6/lMTqes8nX6IZBz1dIGxsTSy9ytifDvElaHfgRcIDtB8ZyDNvH2J5le9bUqQN9g3BEX+tEvkJyNqJX0sZGdF7XC2RJkykS92TbZzbZ5DZgWmV+/XJZRPRY8jVisCRnI7qj26NYCDgWuN72kcNsNgd4Z3mn7cuB+20v83VtRHRX8jVisCRnI7qn232QXwW8A7ha0vxy2SeA6QC2vwWcDewA3Ag8DOzb5Zgiornka8RgSc5GdElXC2TbFwEaZRsDH+hmHBExuuRrxGBJzkZ0T09u0ouIiIiIGBQpkCMiIiIiKlIgR0RERERUpECOiIiokaTnSzpX0jXl/GaSPlV3XBETWQrkiIiIen0HOBh4HMD2VcCetUYUMcGlQI6IiKjXqrYva1i2uJZIIgJIgRwREVG3uyRtCBhA0luAPMwjokbdflBIREREjOwDwDHAJpJuA24G9q43pIiJLQVyREREjWzfBGwraTVgBdsP1h1TxESXLhYRERE1krSOpKOBC4HzJR0laZ2644qYyFIgR0RE1OtU4O/AbsBbyten1RpRxASXLhYRERH1eo7twyvzn5W0R23RRESuIEdERNTsV5L2lLRCOb0V+GXdQUVMZCmQIyIi6vVe4AfAY+V0KvA+SQ9KeqDWyCImqHSxiIiIqJHtNeqOISKWlivIERERNZL0I0k7SEqbHNEnWk5GSS/qZiARERET1DcpHgzyJ0lflLRx3QFFTHTtfFr9H0mXSXq/pDW7FlFERMQEYvvXtvcGtgBuAX4t6RJJ+0qaXG90ERNTywWy7X+m+IQ7DbhC0g8kvW6kfSQdJ+lOSdcMs35rSfdLml9On24r+ojoqORsRD3KB4PsA7wH+ANwFEXBfM4I+yRfI7qkrZv0bP9J0qeAucDRwOaSBHzC9plNdjkB+Dpw0giHvdD2ju3EERGjk7QeMINKntu+YJTdTiA5G9FTkn4MbAx8D9jJ9h3lqtMkzR1h1xNIvkZ0RcsFsqTNgH2BN1J8ot3J9jxJ6wK/A5YpkG1fIGlmh2KNiBZJ+hKwB3AdsKRcbGDEAjk5G1GL79g+u7pA0kq2H7M9a7idkq8R3dPOFeSvAd+luFr8yNBC27eXV5XH6hWSrgRuBw60fe04jhURhTcBG9t+rAvHTs5GdNZngbMblv2OoovFeCVfI8agpQJZ0iTgNtvfa7Z+uOUtmAfMsL1I0g7A/wIbDRPDbGA2wPTp08d4uogJ4yZgMsVDBzopORvRIZKeDawHrCJpc0DlqqcDq3bgFMnXiDFq6SY920uAaZJW7OTJbT9ge1H5+mxgsqQpw2x7jO1ZtmdNnTq1k2FELI8eBuZL+rako4em8R40ORvRUW8AvgKsDxxRmf4T+MR4D558jRi7drpY3AxcLGkO8NDQQttHjvXk5afnv9m2pJdRFOx3j/V4EfGkOeXUUcnZiM6xfSJwoqTdbP9ouO0k/Vu5bVuSrxFj106B/OdyWgEYeiymR9pB0inA1sAUSQuBQyi+9sX2t4C3AP8haTHwCLCn7RGPGRGjs31i+Y3P88tFN9h+fLT9krMRvTdScVzaH1imQE6+RnRPOwXydbZPry6QtPtIO9jea5T1X6cYoiYiOkjS1hQN6i0U/RqnlVehRhvFIjkb0X/UbGHyNaJ72nmS3sEtLouI+h0BvN72a2z/C0Vfx6/WHFNEjE2u+kb02KhXkCVtD+wArNdwk8/TgcXdCiwixmWy7RuGZmz/MY+sjRhYTa8gR0T3tNLF4naKJ+ftDFxRWf4gxZ22EdF/5kr6LvD9cn5vijyOiD4iaQXgLbZ/OMJmF/cqnogojFog274SuFLSD1q5ySci+sJ/AB8APlTOXwj8T33hREQztp+Q9FFg2ALZ9n49DCkiaO8mvZdJOhSYUe4nwLaf243AImLsyifoHVlOEdHffi3pQOA0lh5G9Z76QoqY2NopkI+l6FJxBbCkO+FExHhI+qHtt0q6miY39tjerIawImJke5Q/P1BZZiAXoCJq0k6BfL/tn3ctkojohP3LnzvWGkVEtMz2BnXHEBFLa6dA/o2k/wLOBB4bWmh7XsejiogxsX1H+fPWumOJiNZIWhX4MDDd9mxJGwEb2/5ZzaFFTFjtFMhblT9nVZYZ2KZz4UREJ0h6kGW7WNxPMZLFR2zf1PuoImIYx1N0X3xlOX8bcDqQAjmiJi0XyLb/tZuBRERH/TewEPgBxQ21ewIbAvOA4ygeTxsR/WFD23tI2gvA9sOSMvZxRI1aLpAlfbrZctuHdS6ciOiQnW2/uDJ/jKT5tj8m6RO1RRURzfxD0iqU3/pI2pBKV8aI6L12HjX9UGVaAmwPzOxCTBExfg9LequkFcrprcCj5bo8tjaivxwC/AKYJulk4Fzgo/WGFDGxtdPF4ojqvKSvAL/seEQR0Ql7A0dRPBzEwO+Bt5dXqfLQgYg+UT5J7xnArsDLKbpE7W/7rloDi5jg2rlJr9GqwPqdCiQiOqe8CW+nYVZf1MtYImJ4Q0/SKx81fVbd8UREoeUuFpKulnRVOV0L3EBxI1BE9BlJz5d0rqRryvnNJH2q7rgioqlfSzpQ0jRJaw9NdQcVMZG1cwW5+uCBxcDfbC/ucDwR0RnfAQ4Cvg1g+ypJPwA+W2tUEdFMnqQX0Wfa6YN8q6QXA/9cLroAuKorUUXEeK1q+7KGkaLygTaiz5R9kD9u+7S6Y4mIp7TTxWJ/4GTgmeV0sqQPdiuwiBiXu8qhooaGjXoLcEe9IUVEI9tPUHzbExF9pJ0uFu8GtrL9EICkLwG/A77WjcAiYlw+ABwDbCLpNuBmipEtIqL//FrSgcBpFEOpAmD7nvpCipjY2hkHWRTjHw9ZUi4bfgfpOEl3Dt0o1GS9JB0t6cby5r8t2ognIpqQNAl4v+1tganAJrZfbfvWFvZNzkb03h4UH2ovoHjk9BUUj4UfUfI1onvaKZCPBy6VdKikQynGVT12lH1OALYbYf32wEblNBv4ZhvxREQTtpcAry5fP2T7wTZ2P4HkbERP2d6gydTKDXonkHyN6Ip2btI7UtL5lA0vsK/tP4yyzwWSZo6wyS7ASbYN/F7SWpKeYzt9JSPG5w+S5gCns/RXtmeOtFNyNqL3JK0KfBiYbnu2pI2AjW3/bKT9kq8R3dNygSzp5cC1tueV80+XtJXtS8dx/vWABZX5heWyZZJX0myKT8BMnz59HKeMmBBWBu4GtqksMzBigdyC5GxE5x1P0a3ileX8bRQfbkcskFuQfI0Yo3a6WHwTWFSZX0QPv66xfYztWbZnTZ06tVenjRhItvdtMr1raL2kg3sQQ3I2ojUb2v4y8DiA7YcZ5R6fTku+RiytrZv0yq9pgCeHphnPo6qh+JQ8rTK/frksIrpr9zHul5yN6Lx/SFqFp4Zl3BB4rAPHTb5GjFE7BfJNkj4kaXI57Q/cNM7zzwHeWd5p+3Lg/vSNiuiJsV6dSs5GdN4hwC+AaZJOBs4FPtqB4yZfI8aonSvA/w4cDXyK4lPuuZT9lYYj6RRga2CKpIUUfwQmA9j+FnA2sANwI/AwsG974UfEGLnZwuRsRO9IepXtiymGd9sVeDnFh9f9bd/Vwv7J14guaWcUizuBPYdbL+lg219o2GevUY5pln72fET0RtMryMnZiJ46GtgS+J3tLYCz2tk5+RrRPePtQ1y1O/CFUbeKiK6TtHbjU7gkbWD75nL29BrCioilPS7pGGB9SUc3rrT9oRpiigg6WyD39I7biBjRTyVtb/sBAEmbAj8EXghg+/N1BhcRAOwIbAu8gWKYt4joE50skJv2aYyIWnyeokh+I7AxcBKwd70hRURV2c/4VEnX276y7ngi4im5ghyxHLJ9lqTJwK+ANYA32/5jzWFFRHOPSDoXeJbtF0raDNjZ9mfrDixiomrnSXrp0xjR5yR9jaW/zVkT+DOwn6T0aYzoT98BDgK+DWD7Kkk/AFIgR9SknSvI6dMY0f/mNsynX2NE/1vV9mXSUl/ELq4rmIhor0BOn8aIPmf7RABJqwGP2l5Szk8CVqoztogY1l3l0/OGnqT3FiAP9IioUTvjIKdPY8TgOJfi7vhF5fwqFLn7ytoiiojhfAA4BthE0m3AzeQCVEStRi2Q06cxYiCtbHuoOMb2Ikmr1hlQRCyr/Hbn/ba3Lb/5WcH2g3XHFTHRtXIFOX0aIwbPQ5K2sD0PQNKWwCM1xxQRDWwvkfTq8vVDdccTEYVRC+T0aYwYSAcAp0u6nWIIxmcDe9QaUUQM5w+S5lCMBvVkkWz7zPpCipjY2rlJL30aIwaE7cslbUJxQy3ADbYfrzOmiBjWysDdwDaVZQZSIEfUpJ0COX0aI/qcpG1snydp14ZVzy/vGUiDG9FnbO870npJB9v+Qq/iiYj2CuT0aYzof68BzgN2arIuV6QiBtPuQArkiB5qp0A+gPRpjOhrtg8pf454RSoiBopG3yQiOqmdcZDTpzGiz0n68EjrbR/Zq1giomM8+iYR0UmtjIOcPo0Rg2ONEdalkY0YTLmCHNFjrVxBTp/GiAFh+zMAkk4E9rd9Xzn/DOCIGkOLiGFIWtv2PQ3LNrB9czl7eg1hRUxoK4y2QbVPY5PpXaPtL2k7STdIulHSx5us30fS3yXNL6f3jO2tRETFZkPFMYDte4HNR9sp+RpRi59KevrQjKRNgZ8Ozdv+/HA7JmcjuqOVLhZj7tNYPkzkG8DrgIXA5ZLm2L6uYdPTbO/XQrwR0ZoVJD2jLIyRtDaj5HvyNaI2n6cokt9IcZ/PScDeo+2UnI3onla6WIynT+PLgBtt3wQg6VRgF6AxeSOis44Afidp6KvZ3YHPjbJP8jWiBrbPkjSZ4uFbawBvtv3HFnZNzkZ0SSuPmh5Pn8b1gAWV+YXAVk22203SvwB/BP7T9oLGDSTNBmYDTJ8+fbSwIyY02ydJmstTT+batclVpUYdy1dIzkaMRtLXWPpC05rAn4H9ypvgPzTKIdLGRnRJO+MgL9OnUdKofRpb8FPgFNuPSXofcCJLP25z6HzHAMcAzJo1K3fjR4yiLIg7fSWppXwtz5+cjRjZ3Ib5K7pwjrSxEWPQToHcdp9G4DZgWmV+/XLZk2zfXZn9LvDlNmKKiM5Jvkb0kO0TASStBjxqe0k5PwlYqYVDJGcjumTUUSwqhvo0Hi7pcOASRk+0y4GNJG0gaUVgT2BOdQNJz6nM7gxc30ZMEdE5ydeIepwLrFKZXwX4dQv7JWcjuqSdJ+m13afR9mJJ+wG/BCYBx9m+VtJhwFzbc4APSdoZWAzcA+wzhvcREeOUfI2ozcq2Fw3N2F4kadXRdkrORnRPO10sxtSn0fbZwNkNyz5deX0wcHA7x4yI7ki+RtTiIUlb2J4HIGlL4JFWdkzORnRHWwVyREREdNwBwOmSbqd4rPSzgT1qjShigkuBHBERUSPbl0vahOIhIQA32H68zpgiJroUyBERETWQtI3t8yTt2rDq+eU4yGfWElhEpECOiIioyWuA84CdmqwzkAI5oiYpkCMiImpg+5Dy5751xxIRS0uBHBERUQNJHx5pve0jexVLRCwtBXJEREQ91hhhXR73HFGjFMgRERE1sP0ZAEknAvvbvq+cfwbF02sjoibtPGo6IiIiOm+zoeIYwPa9wOb1hRMRKZAjIiLqtUJ51RgASWuTb3gjapUEjIiIqNcRwO8knV7O7w58rsZ4Iia8FMgRERE1sn2SpLnANuWiXW1fV2dMERNdCuSIiIialQVxiuKIPpE+yBERERERFSmQIyIiIiIqUiBHRERERFSkQI6IiIiIqEiBHBERERFR0fUCWdJ2km6QdKOkjzdZv5Kk08r1l0qa2e2YIqK55GvEYEnORnRHVwtkSZOAbwDbA5sCe0natGGzdwP32n4e8FXgS92MKSKaS75GDJbkbET3dPsK8suAG23fZPsfwKnALg3b7AKcWL4+A3itJHU5rohYVvI1YrAkZyO6pNsPClkPWFCZXwhsNdw2thdLuh9YB7irupGk2cDscnaRpBu6EnGMZAoN/y7RnL7yb5083IxOHmwEHctXSM72geRriwY0XyFt7PImOduiXuTswDxJz/YxwDF1xzGRSZpre1bdccRgSM7WK/ka7Ui+1i8521+63cXiNmBaZX79clnTbSQ9DVgTuLvLcUXEspKvEYMlORvRJd0ukC8HNpK0gaQVgT2BOQ3bzAGGrpW/BTjPtrscV0QsK/kaMViSsxFd0tUuFmV/p/2AXwKTgONsXyvpMGCu7TnAscD3JN0I3EOR4NGf8vXbciz5utxJvi7nkrPLneRsH1E+SEZEREREPCVP0ouIiIiIqEiBHBERERFRkQI5IiIiIqIiBXJEREREj0naRNJrJa3esHy7umKKp6RAjrZJ2rfuGCJiZJKeWXcMEdGcpA8BPwE+CFwjqfqI8M/XE1VUZRSLaJukv9ieXnccEVGQtHbjIuAKYHOKv/P39D6qiBiOpKuBV9heJGkmcAbwPdtHSfqD7c3rjTAG5lHT0VuSrhpuFfCsXsYSEaO6C7i1Ydl6wDzAwHN7HlFEjGQF24sAbN8iaWvgDEkzKNrZqFkK5BjOs4A3APc2LBdwSe/DiYgRHAS8DjjI9tUAkm62vUG9YUXEMP4m6SW25wOUV5J3BI4DXlRrZAGkQI7h/QxYfSh5qySd3/NoImJYto+QdBrwVUkLgEMorhxHRH96J7C4usD2YuCdkr5dT0hRlT7IERHLEUk7A58AZtp+dt3xREQMohTIERHLAUmbUPQ7vhRYAmxo+xpJ29n+Rb3RRUQMlgzzFhEx4BqHjAJeb/uacnWGjIqIaFMK5AlKUls32knaWtLPuhVPRIzLe4Etbb8J2Br4f5L2L9fljviIHksbO/hyk94EZfuVdccQER2TIaMi+kja2MGXK8gTlKRF5c+tJZ0v6QxJ/yfpZEkq121XLpsH7FrZdzVJx0m6TNIfhp4AJOkoSZ8uX79B0gWS8n8sovv+JuklQzNlsbwjMIUMGRXRc2ljB1+uIAcUT9t6AXA7cDHwKklzge8A2wA3AqdVtv8kcJ7td0laC7hM0q+Bg4HLJV0IHA3sYPuJ3r2NiAkrQ0ZF9K+0sQMonzwC4DLbC8tEmw/MBDYBbrb9JxdDnXy/sv3rgY9Lmg+cD6wMTLf9MEVfyHOAr9v+c8/eQcQEVubvX4dZd3Gv44mIpaSNHUC5ghwAj1VeL2H0/xcCdrN9Q5N1LwLuBtbtUGwRERGDLG3sAMoV5BjO/wEzJW1Yzu9VWfdL4IOVflSblz9nAB+h+Dppe0lb9TDeiIiIQZE2ts+lQI6mbD8KzAbOKm8guLOy+nBgMnCVpGuBw8tEPhY40PbtwLuB70paucehRyyXMmxUxPIjbWz/y5P0IiKWQ+VQbwfa3rHmUCIiBk6uIEdEDIAMGxUR0Tu5SS8iYvBk2KiIiC7KlYKIiMGTYaMiIrooV5AjIgZPho2KiOiiXEGOiFg+ZNioiIgOSYEcEbEcyLBRERGdk2HeIiIiIiIqcgU5IiIiIqIiBXJEREREREUK5IiIiIiIihTIEREREREVKZAjIiIiIipSIEdEREREVKRAjoiIiIio+P+PJre5Img4agAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析用户点击环境变化是否明显，这里随机采样10个用户分析这些用户的点击环境分布\n",
    "sample_user_ids = np.random.choice(tst_click['user_id'].unique(), size=5, replace=False)\n",
    "sample_users = user_click_merge[user_click_merge['user_id'].isin(sample_user_ids)]\n",
    "cols = ['click_environment','click_deviceGroup', 'click_os', 'click_country', 'click_region','click_referrer_type']\n",
    "for _, user_df in sample_users.groupby('user_id'):\n",
    "    plot_envs(user_df, cols, 2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出绝大多数数的用户的点击环境是比较固定的。思路：可以基于这些环境的统计特征来代表该用户本身的属性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用户点击新闻数量的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb191179a90>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAASw0lEQVR4nO3da4yc1X3H8e8fr+/GVxZj1nZsgnshVCl0RYyS8iLkBm1qKpGIqCpWimSpJU1SWjXQvEjUV0nUQEMTkTghFamilIRQYVW0gQJRlRdxsgbCNYSNa8CLsZeLL/EFbHz6Yo6dsbPjZ9be2Znn+PuRrH2e85yZ55x9xr+ZOXP2TKSUkCSV64xuN0CS1FkGvSQVzqCXpMIZ9JJUOINekgrX1+0GAJx11llpxYoV3W6GJNXKpk2bXk4p9VfV64mgX7FiBUNDQ91uhiTVSkQ81049h24kqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSpcrYP+p1te5eb7nuGNQ4e73RRJ6lm1DvqHn3uNWx8c5tBhg16SWql10EuSqhn0klQ4g16SCmfQS1Lhigh6v99cklqrddBHdLsFktT7ah30kqRqBr0kFc6gl6TCGfSSVLgigt5JN5LUWq2DPnDajSRVqXXQS5KqGfSSVDiDXpIKV0TQJ9dAkKSWah30LoEgSdVqHfSSpGoGvSQVzqCXpMIZ9JJUuCKC3jk3ktRaEUEvSWrNoJekwhn0klS4toI+Iv4mIp6MiCci4jsRMSMiVkbExogYjog7I2Jarjs97w/n4ys62gNJ0glVBn1EDAAfBwZTShcCU4BrgM8Dt6SUzgdeA67LN7kOeC2X35LrSZK6pN2hmz5gZkT0AbOAbcC7gbvy8TuAq/L2mrxPPn55RGcXK3CpG0lqrTLoU0ojwD8Bz9MI+F3AJmBnSulQrrYVGMjbA8AL+baHcv1Fx99vRKyLiKGIGBodHT2pxnf4+UOSitDO0M0CGq/SVwLnArOBD5zqiVNK61NKgymlwf7+/lO9O0lSC+0M3bwH+L+U0mhK6SBwN/BOYH4eygFYCozk7RFgGUA+Pg94ZUJbLUlqWztB/zywOiJm5bH2y4GngIeAq3OdtcA9eXtD3icffzC5YLwkdU07Y/QbaXyo+jDweL7NeuBTwA0RMUxjDP72fJPbgUW5/Abgxg60W5LUpr7qKpBS+gzwmeOKNwOXjFH3APChU2/aOPh+QZJaqvVfxjrnRpKq1TroJUnVDHpJKpxBL0mFM+glqXBFBH1y2o0ktVTroHepG0mqVuuglyRVM+glqXAGvSQVzqCXpMIVEfSujSlJrdU66J10I0nVah30kqRqBr0kFc6gl6TCFRH0fhYrSa3VOujDNRAkqVKtg16SVM2gl6TCGfSSVDiDXpIKV0TQJ9dAkKSWah30TrqRpGq1DnpJUjWDXpIKZ9BLUuEMekkqXBFB75wbSWqt1kHvpBtJqlbroJckVTPoJalwBr0kFa6toI+I+RFxV0T8PCKejohLI2JhRNwfEc/mnwty3YiIWyNiOCIei4iLO9sFSdKJtPuK/kvAf6eUfgd4O/A0cCPwQEppFfBA3ge4AliV/60DbpvQFo/BpW4kqbXKoI+IecBlwO0AKaU3Uko7gTXAHbnaHcBVeXsN8K3U8GNgfkQsmeB2H2lcR+5WkkrSziv6lcAo8K8R8UhEfCMiZgOLU0rbcp2XgMV5ewB4oen2W3OZJKkL2gn6PuBi4LaU0kXAXn49TANAaqwTPK4BlIhYFxFDETE0Ojo6nptKksahnaDfCmxNKW3M+3fRCP7tR4Zk8s8d+fgIsKzp9ktz2TFSSutTSoMppcH+/v6Tbb8kqUJl0KeUXgJeiIjfzkWXA08BG4C1uWwtcE/e3gBcm2ffrAZ2NQ3xSJImWV+b9f4a+HZETAM2Ax+l8STx3Yi4DngO+HCuey9wJTAM7Mt1Oyq52o0ktdRW0KeUHgUGxzh0+Rh1E3D9qTWrPc65kaRq/mWsJBXOoJekwhn0klQ4g16SCldG0DvpRpJaqnXQu9SNJFWrddBLkqoZ9JJUOINekgpXRND7WawktVbroA8XQZCkSrUOeklSNYNekgpn0EtS4Qx6SSpcEUGfnHYjSS3VOuhdAkGSqtU66CVJ1Qx6SSqcQS9JhTPoJalwRQR9crUbSWqp1kHvpBtJqlbroJckVTPoJalwBr0kFc6gl6TCFRH0rnUjSa3VOuhd60aSqtU66CVJ1Qx6SSqcQS9JhTPoJalwRQS9k24kqbW2gz4ipkTEIxHxn3l/ZURsjIjhiLgzIqbl8ul5fzgfX9GhthOudiNJlcbziv4TwNNN+58HbkkpnQ+8BlyXy68DXsvlt+R6kqQuaSvoI2Ip8EfAN/J+AO8G7spV7gCuyttr8j75+OW5viSpC9p9Rf/PwN8Dh/P+ImBnSulQ3t8KDOTtAeAFgHx8V65/jIhYFxFDETE0Ojp6cq2XJFWqDPqI+GNgR0pp00SeOKW0PqU0mFIa7O/vn8i7liQ16WujzjuBP4mIK4EZwFzgS8D8iOjLr9qXAiO5/giwDNgaEX3APOCVCW95k+RiN5LUUuUr+pTSTSmlpSmlFcA1wIMppT8DHgKuztXWAvfk7Q15n3z8wdSpJHbkX5Iqnco8+k8BN0TEMI0x+Ntz+e3Aolx+A3DjqTVRknQq2hm6OSql9EPgh3l7M3DJGHUOAB+agLZJkiZAEX8ZK0lqrYig97NYSWqt1kHvZ7GSVK3WQS9JqmbQS1LhDHpJKpxBL0mFM+glqXC1DnpXP5akarUOeklSNYNekgpn0EtS4Qx6SSpcEUHvWjeS1Fqtg945N5JUrdZBL0mqZtBLUuEMekkqnEEvSYUrIugTTruRpFZqHfQudSNJ1Wod9JKkaga9JBXOoJekwhn0klS4IoLetW4kqbVaB72zbiSpWq2DXpJUzaCXpMIZ9JJUOINekgpXRNA76UaSWqt10IffMSVJlSqDPiKWRcRDEfFURDwZEZ/I5Qsj4v6IeDb/XJDLIyJujYjhiHgsIi7udCckSa2184r+EPC3KaULgNXA9RFxAXAj8EBKaRXwQN4HuAJYlf+tA26b8FZLktpWGfQppW0ppYfz9h7gaWAAWAPckavdAVyVt9cA30oNPwbmR8SSiW64JKk94xqjj4gVwEXARmBxSmlbPvQSsDhvDwAvNN1say47/r7WRcRQRAyNjo6Ot93HSK6BIEkttR30ETEH+D7wyZTS7uZjqZG040rblNL6lNJgSmmwv79/PDdtatNJ3UySTittBX1ETKUR8t9OKd2di7cfGZLJP3fk8hFgWdPNl+YySVIXtDPrJoDbgadTSjc3HdoArM3ba4F7msqvzbNvVgO7moZ4JEmTrK+NOu8E/hx4PCIezWX/AHwO+G5EXAc8B3w4H7sXuBIYBvYBH53IBkuSxqcy6FNKP4KWf5l0+Rj1E3D9KbZLkjRBav2XsUc450aSWisi6CVJrRn0klQ4g16SCmfQS1LhDHpJKlwRQe9SN5LUWq2DPlzsRpIq1TroJUnVDHpJKpxBL0mFM+glqXC1Dvq9rx8C4I1Dh7vcEknqXbUO+gWzpgJw2PmVktRSrYN+xtQpALzuK3pJaqnWQT+9rxH0Dt1IUmv1Dvqpjea//KvXu9wSSepdtQ76aVMazT/Dv5CVpJZqHfQLZ08DYMsre7vcEknqXbUO+vl51s3BNx2jl6RWah30s6Y1vtv82R2/6nJLJKl31TroARbNnsbTL+7udjMkqWfVPujPmTeDzS87Ri9JrdQ+6N+xchEAv9i+p8stkaTeVPug/+DblwDw9f/d3OWWSFJvqn3QX7R8AQDf27T16CJnkqRfq33QA/zjmrcB8MEv/6jLLZGk3lNE0F976QrOP3sOm0f3ctkXHmLPgYPdbpIk9Ywigh7g3o//IcsXzuL5V/fxe5+9jy/e94yLnUkSEKkH1nIfHBxMQ0NDE3JfN9//C2594Nmj++/53bN5/9vO4f0XnsPcGVMn5ByS1AsiYlNKabCyXmlBD7DvjUP8y4PD3P3wVrbv/vXKlufMncFFy+dz4cA8Llo2n7cNzGPO9D6mnOGiaJLq57QO+mbbdx9gw6Mv8pMtr/LY1p3HBP8R5/XPZmD+TM6aM53zz57DnOl9rDxrNnNnTmXZgpksmjO9I22TpFNh0Ldw8M3DPDGyi0ee38nW1/azY88Bntq2m9cPHmZk5/4T3nbx3OmcM3cG0Fg5c/nCWUePLVs46+hqmkDjiePMY58gBubPPPqtWJJ0qtoN+r4OnfwDwJeAKcA3Ukqf68R5TsbUKWdw0fIFR+ffN9v/xpvsP/gmW17Zy659B9m26wDbdx8AGn95u//gmwBseXkvz726j0de2ElKsGt/+7N8zpz+m7/y/jOnc+78mWPWnzltCr+1eM4J73PZgmOfZE7kwoF59I1jqGp63xTmzfKzDanOJjzoI2IK8BXgvcBW4KcRsSGl9NREn2uizZw2hZnTprQdmkfs2neQnfvfOLo/snM/o3uOHSJ6/pV9vLbvN58Qhkd/xd7XDx19Emm2bed+dux5nYd+vqPluQ8d7vw7sgWzpk7IO5HxPCFVmTV9CqvOPnNC7msiNJ6sZ0zqOd/aP4czZ3TktZpOwYy+KZzRY5/7deJRcgkwnFLaDBAR/w6sAXo+6E/WvFlTj3nV+5ZFsyft3Dt2H2DHnva+SvGpF3dz8HD7U04PvZl4YmQXE/EFXk+M7GbX/oPjevfTyos797PHv4JWj4qA8/tP/C682ccvX8UH335uB1vUmaAfAF5o2t8KvOP4ShGxDlgHsHz58g404/Rw9twZnD23vVeSFw7M63BrJs+BMd4Bdcure9/g+Vf3Teo5n3tlLzvHeIeo7npx535Gx/kd1vNmdn5otGvv+1JK64H10PgwtlvtUD310ofa586f2fIzlk5Zfd6iST2f6q0Tfxk7Aixr2l+ayyRJXdCJoP8psCoiVkbENOAaYEMHziNJasOED92klA5FxMeAH9CYXvnNlNKTE30eSVJ7OjJGn1K6F7i3E/ctSRqfYlavlCSNzaCXpMIZ9JJUOINekgrXE6tXRsQo8NxJ3vws4OUJbE4d2OfTg30+PZxKn9+SUuqvqtQTQX8qImKonWU6S2KfTw/2+fQwGX126EaSCmfQS1LhSgj69d1uQBfY59ODfT49dLzPtR+jlySdWAmv6CVJJ2DQS1Lhah30EfGBiHgmIoYj4sZut2e8ImJLRDweEY9GxFAuWxgR90fEs/nnglweEXFr7utjEXFx0/2szfWfjYi1TeV/kO9/ON920r/IMiK+GRE7IuKJprKO97HVObrY589GxEi+1o9GxJVNx27K7X8mIt7fVD7m4zsvAb4xl9+ZlwMnIqbn/eF8fMUkdZmIWBYRD0XEUxHxZER8IpcXe61P0Ofeu9YppVr+o7EE8i+B84BpwM+AC7rdrnH2YQtw1nFlXwBuzNs3Ap/P21cC/wUEsBrYmMsXApvzzwV5e0E+9pNcN/Jtr+hCHy8DLgaemMw+tjpHF/v8WeDvxqh7QX7sTgdW5sf0lBM9voHvAtfk7a8Cf5m3/wr4at6+BrhzEvu8BLg4b58J/CL3rdhrfYI+99y1ntT/9BP8S74U+EHT/k3ATd1u1zj7sIXfDPpngCVND6Rn8vbXgI8cXw/4CPC1pvKv5bIlwM+byo+pN8n9XMGxodfxPrY6Rxf73Oo//zGPWxrf43Bpq8d3DrmXgb5cfrTekdvm7b5cL7p0ze8B3ns6XOsx+txz17rOQzdjfQn5QJfacrIScF9EbIrGl6UDLE4pbcvbLwGL83ar/p6ofOsY5b1gMvrY6hzd9LE8TPHNpuGF8fZ5EbAzpXTouPJj7isf35XrT6o8jHARsJHT5Fof12fosWtd56AvwbtSShcDVwDXR8RlzQdT4+m66Pmvk9HHHvk93ga8Ffh9YBvwxa62pkMiYg7wfeCTKaXdzcdKvdZj9LnnrnWdg772X0KeUhrJP3cA/wFcAmyPiCUA+eeOXL1Vf09UvnSM8l4wGX1sdY6uSCltTym9mVI6DHydxrWG8ff5FWB+RPQdV37MfeXj83L9SRERU2kE3rdTSnfn4qKv9Vh97sVrXeegr/WXkEfE7Ig488g28D7gCRp9ODLTYC2NcT9y+bV5tsJqYFd+u/oD4H0RsSC/RXwfjXG8bcDuiFidZydc23Rf3TYZfWx1jq44EkTZn9K41tBo5zV5FsVKYBWNDx3HfHznV6wPAVfn2x//+zvS56uBB3P9jsu//9uBp1NKNzcdKvZat+pzT17rbnxoMYEfflxJ45PuXwKf7nZ7xtn282h8uv4z4Mkj7acxzvYA8CzwP8DCXB7AV3JfHwcGm+7rL4Dh/O+jTeWD+UH2S+DLdOGDOeA7NN6+HqQxxnjdZPSx1Tm62Od/y316LP8nXdJU/9O5/c/QNDOq1eM7P3Z+kn8X3wOm5/IZeX84Hz9vEvv8LhpDJo8Bj+Z/V5Z8rU/Q55671i6BIEmFq/PQjSSpDQa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKtz/A1/NmoIeUlAfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_click_item_count = sorted(user_click_merge.groupby('user_id')['click_article_id'].count(), reverse=True)\n",
    "plt.plot(user_click_item_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可以根据用户的点击文章次数看出用户的活跃度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb182366748>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#点击次数在前50的用户\n",
    "plt.plot(user_click_item_count[:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "点击次数排前50的用户的点击次数都在100次以上。思路：我们可以定义点击次数大于等于100次的用户为活跃用户，这是一种简单的处理思路， 判断用户活跃度，更加全面的是再结合上点击时间，后面我们会基于点击次数和点击时间两个方面来判断用户活跃度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb18701a208>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#点击次数排名在[25000:50000]之间\n",
    "plt.plot(user_click_item_count[25000:50000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出点击次数小于等于两次的用户非常的多，这些用户可以认为是非活跃用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb19382aa20>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "item_click_count = sorted(user_click_merge.groupby('click_article_id')['user_id'].count(), reverse=True)\n",
    "plt.plot(item_click_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前一百位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb1825312e8>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(item_click_count[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前二十位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb1824c4d68>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(item_click_count[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3500位以后"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb1824ffc88>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(item_click_count[3500:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 新闻共现频次：两篇新闻连续出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>433597.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.184139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.851753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2202.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count\n",
       "count  433597.000000\n",
       "mean        3.184139\n",
       "std        18.851753\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max      2202.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = user_click_merge.sort_values('click_timestamp')\n",
    "tmp['next_item'] = tmp.groupby(['user_id'])['click_article_id'].transform(lambda x:x.shift(-1))\n",
    "union_item = tmp.groupby(['click_article_id','next_item'])['click_timestamp'].agg({'count'}).reset_index().sort_values('count', ascending=False)\n",
    "union_item[['count']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb19107b1d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画个图直观地看一看\n",
    "x = union_item['click_article_id']\n",
    "y = union_item['count']\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb182491d30>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(union_item['count'].values[40000:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大概有70000个pair至少共现一次。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb0d5333e80>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ApAeAf0xP9wBzMvvNTjX6qB8EpkgqpF5Fdvuzotij8ByFmY1vwzH0dCuZYSdJMzPrbgY2p+U1wHJJNZLmAQuAZ4BngQXpCqdqisNYayIigKeAW9L+K4AnhqG9A1bty2PNzIbWo5A0CfgQ8PuZ8lckLQIC2Nm1LiK2SHoUeBFoB+6IiI50nDuBJ4E8sCoitqRjfQZ4RNJfAD8DHhxKe89UTSHHm6fa+9/QzOwcNqSgiIjjFCeds7XfKbP9F4Ev9lJfC6ztpb6D4lVRFeHLY83MfGd2WTVVvurJzMxBUYY/wsPMzEFRloeezMwcFGX5hjszMwdFWf4IDzMzB0VZNYU87Z1Be4fDwszGLwdFGV3fm93qoDCzccxBUUZ1Cgpf+WRm45mDooyaQh7w92ab2fjmoCija+jJl8ia2XjmoCijpqr46znloSczG8ccFGXU1RQ/CuvNU20VbomZWeU4KMqor6sB4MCxlgq3xMyschwUZTScVwyK5mOtFW6JmVnlOCjKmDapGoADb7pHYWbjl4OijKp8jqkTqzz0ZGbjmoOiH/V1NRz00JOZjWMOin7U19W4R2Fm45qDoh/15zkozGx8G3JQSNop6QVJz0tqTLVpktZJ2pZ+Tk11SbpXUpOkTZIuzxxnRdp+m6QVmfoV6fhNaV8Ntc1nor6umgMeejKzcWy4ehS/FhGLImJxen4XsD4iFgDr03OAG4AF6bESuA+KwQLcDVwFXAnc3RUuaZuPZfZbOkxtHpD6uhqOtbRzqs0f42Fm49NIDT0tA1an5dXATZn6Q1G0AZgiaSZwPbAuIg5FxGFgHbA0rZscERsiIoCHMsc6K+rripfINvsSWTMbp4YjKAL4kaSNklam2oyI2JuWXwdmpOVZwKuZfXenWrn67l7q3UhaKalRUmNzc/NQz6ebt02uBWDPkZPDelwzs7FiOILifRFxOcVhpTskXZ1dmXoCMQyv06eIuD8iFkfE4oaGhmE99qLZU5Dg2VcODetxzczGiiEHRUTsST/3A9+nOMewLw0bkX7uT5vvAeZkdp+dauXqs3upnzVTJ1XzrrdPZsMrB8/my5qZjRpDCgpJkySd17UMXAdsBtYAXVcurQCeSMtrgNvS1U9LgDfSENWTwHWSpqZJ7OuAJ9O6o5KWpKudbssc66xZMn8aG3cd9vdSmNm4NNQexQzgXyT9HHgG+L8R8UPgS8CHJG0DPpieA6wFdgBNwAPAJwAi4hDw58Cz6fFnqUba5m/TPtuBHwyxzWfsqnnTOdXWyeY9b5ztlzYzq7jCUHaOiB3Ae3upHwSu7aUewB19HGsVsKqXeiNw6VDaOVS/NPM8ALbvP84VF02rZFPMzM4635k9ALOmTKAqL3YcOF7pppiZnXUOigEo5HNcOG0irxw4VummmJmddQ6KAZpXX8fOAycq3Qwzs7POQTFA8xsm8crB43R2jugtIWZmo46DYoDm1U+itb2T197wHdpmNr44KAZofv0kAF7a+2aFW2JmdnY5KAZo0YVTqKsp8OMX91W6KWZmZ5WDYoBqCnmuedfbWPfSPto7OivdHDOzs8ZBcQaWXvp2Dh1v5UfuVZjZOOKgOAPX/tLbuHTWZD7z+CZ2+uY7MxsnHBRnoKaQ577fvoJ8Tvz+tzdyorW90k0yMxtxDoozNGfaRO5dfhlb973JveubKt0cM7MR56AYhKsvaeA3LpvFqv/3Ci/tPVrp5piZjSgHxSD98fXvpK6mwEf+5l/416YDlW6OmdmIcVAM0gVTJvCjT13N1InV/O9/3Vnp5piZjRgHxRDU19Xw4fdewD9t3c+RE62Vbo6Z2YhwUAzRTYtm0dYRfLdxd6WbYmY2IhwUQ3TprMlcfUkD9/z4FzTt9/dVmNm5Z9BBIWmOpKckvShpi6Q/TPUvSNoj6fn0uDGzz2clNUnaKun6TH1pqjVJuitTnyfp6VT/jqTqwbZ3pEjiL2++lHxO3PC1n3L7t57l5dd9JZSZnTuG0qNoB/4oIhYCS4A7JC1M6+6JiEXpsRYgrVsOvBtYCnxDUl5SHvg6cAOwELg1c5wvp2O9AzgM3D6E9o6Y2VMn8qNPXc1vX3URjbsO85nHX6D49eBmZmPfoIMiIvZGxHNp+U3gJWBWmV2WAY9EREtEvAI0AVemR1NE7IiIVuARYJkkAdcAj6X9VwM3Dba9I23m+RP4wkfezV03vIufv3qEf9raXOkmmZkNi2GZo5A0F7gMeDqV7pS0SdIqSVNTbRbwama33anWV306cCQi2kvqvb3+SkmNkhqbmyv7B/o/Xz6bi6ZP5E//zxZOtnZUtC1mZsNhyEEhqQ54HPhkRBwF7gMuBhYBe4GvDvU1+hMR90fE4ohY3NDQMNIvV1Z1Icdf3vzL7Dx4gv/1k20VbYuZ2XAYUlBIqqIYEn8XEd8DiIh9EdEREZ3AAxSHlgD2AHMyu89Otb7qB4Epkgol9VHvV99Rz02LLuDBf3mF1474q1PNbGwbylVPAh4EXoqIv87UZ2Y2uxnYnJbXAMsl1UiaBywAngGeBRakK5yqKU54r4nibPBTwC1p/xXAE4Nt79n2R9e9kwj4k3/YTEenJ7bNbOwaSo/iV4HfAa4puRT2K5JekLQJ+DXgUwARsQV4FHgR+CFwR+p5tAN3Ak9SnBB/NG0L8Bng05KaKM5ZPDiE9p5Vc6ZN5H/c+C7Wv7yfL//w5Uo3x8xs0HSuXca5ePHiaGxsrHQzAIgIPv/EFr69YRe3Xnkht79vHu94W12lm2Vm1oOkjRGxuLd1hd6KNjwkcfeHF3KspZ3HN+7m37Yf4Mef/o8U8r4h3szGDv/FGmGFfI57fmsR9956GTsPnuAfN+2tdJPMzM6Ig+IsuW7hDN719vP44tqXePXQiUo3x8xswBwUZ0kuJ+699TJOtXbw/q88xX95YAMHj7VUullmZv1yUJxFl8w4j8c/8Sv8wbUL2LjrMDd/419p2v9mpZtlZlaWg+Isu2TGeXz6Q5fwyMolnGhtZ8WqZ2lp90d9mNno5aCokMsunMpff3QRe46c9Jcemdmo5qCooPcvqOfyC6dw7/ptHD3VVunmmJn1ykFRQZL4/IffzYFjLfzBwz/jJy/vq3STzMx68A13FbZozhQ+9cFL+Nr6bfzT1mZ++6oLWTRnCr/+nplMrPbbY2aV54/wGCVa2zv57Pde4PHnivMVE6ryvGf2+Vw4bSIfu3o+F02fSE0hX+FWmtm5qtxHeDgoRpnW9k427T7Cmp+/xouvHeWlvUc53tpBTSHH7/3qPD75wQXUVjkwzGx4+bOexpDqQo7Fc6exeO40APYfPcWTL+7juV2H+eY/b+epl/dz+UVT+MA738bCmZOZeX6tPzvKzEaUexRjyA83v87X1m9jz+ETHD1V/IbYfE5cMKWWefV1/PF1l/Ce2VMq20gzG5M89HSOaevo5Lldh9l18AT/fqj42LDjIMda2vnwey5g9tQJTK+rYXpdNfV11cyrr2PapOpKN9vMRjEPPZ1jqvI5rpo/navmTz9d23/0FJ9/YgvrXtrHoeOtPfaZMbmGhvNqmHFeLdf80tv4zSvmUF3wkJWZ9c89inNQS3sHh4+3ceBYC83HWti+/xgv7j3K4eOt7Dp0gh3Nx7m4YRJfueW9XHHR1Eo318xGAQ892WkRwVNb9/Mn/7CF1944yZJ505k1dQLz6icxv34Ss6dOZFpdNdMmVjOh2ldXmY0XDgrr4VhLO1/78S9o3HWY146cZN/Rnh95XluVY/qkGqZOqmLqxGrePrmW+Q111BRyTKjOM2fqRCbW5Kkt5JlUk+eCKROo8hVYZmPSmJ6jkLQU+BqQB/42Ir5U4SadE+pqCnzu1xeefn6spZ1Xmo+z942THD7RysHjrRw+3sqh420cOt7CoRNtvLR3P9/dWP4DDPM5UciJ+Q11TJ9UzcTqPJNqCtRW5Xn75FqmTKxiQlWe2uo8tSlwusKotpCnpipHTSFPPqeR/hWY2QCN6qCQlAe+DnwI2A08K2lNRLxY2Zade+pqCvzy7PP55dnn97lNRHC8tYOOjuDoqTZ2Hz7JqbYOWto7OHqqndeOnKS9IzjZ1sH25mMcPVmcJzne2s7J1k4OnMEXNVXniyEyoSrPxOo8tennhOo8UyZWc8GUWqrzOQq5HIW8Tm8/MT0KuRz5vKjK5cjnRFVe1FYV989LSJBTMYyk4uduiWJNAgEIhMhl1ivVlCM9f6ve7Xi89RrKvIbZWDSqgwK4EmiKiB0Akh4BlgEOigqQRF1N8Z/M+ROrmDNt4hntf6qtg+Mt7Zxs6+BUWwcnWzs50dpO87EW3jzVnkKnk1NtHZxq6+RkazsnWjs42dbBydYOTrR28OapdnYePM6Tm1to6+xkLI6c9ho8KlkmBY8ygVQSPJwOo+77dW1beryRDqoRj8ERfoGRbv/Z+P3/wbUL+PB7Lxj2Y4/2oJgFvJp5vhu4qnQjSSuBlQAXXnjh2WmZnbHaqvywf/xIZ2fQ1tlJa3snJ9s6ONFSDJT2zk7aO4P2jqC9s5O2jkgB1EFHZxABAXSmhaB7rWuZiGKts/izqx6nt4lMLR2v2/o+jhfd9zt9nChzvEyta5m0X9njnd525Ix0Xo/0XOqI///GiP/+iy9w/oSqETn+aA+KAYmI+4H7oTiZXeHm2FmUy4maXJ6aQp7zaqvgvEq3yOzcM9ovUdkDzMk8n51qZmZ2loz2oHgWWCBpnqRqYDmwpsJtMjMbV0b10FNEtEu6E3iS4uWxqyJiS4WbZWY2rozqoACIiLXA2kq3w8xsvBrtQ09mZlZhDgozMyvLQWFmZmU5KMzMrKxz7tNjJTUDuwa5ez1wYBibU2nn0vmcS+cC59b5+FxGrzM5n4sioqG3FedcUAyFpMa+PmZ3LDqXzudcOhc4t87H5zJ6Ddf5eOjJzMzKclCYmVlZDoru7q90A4bZuXQ+59K5wLl1Pj6X0WtYzsdzFGZmVpZ7FGZmVpaDwszMynJQJJKWStoqqUnSXZVuz5mStFPSC5Kel9SYatMkrZO0Lf2cWul29kXSKkn7JW3O1Hptv4ruTe/VJkmXV67lPfVxLl+QtCe9P89LujGz7rPpXLZKur4yre6dpDmSnpL0oqQtkv4w1cfqe9PX+Yy590dSraRnJP08ncufpvo8SU+nNn8nfUUDkmrS86a0fu6AX6z41Yvj+0HxI8y3A/OBauDnwMJKt+sMz2EnUF9S+wpwV1q+C/hypdtZpv1XA5cDm/trP3Aj8AOKXxO8BHi60u0fwLl8AfjjXrZdmP691QDz0r/DfKXPIdO+mcDlafk84BepzWP1venrfMbc+5N+x3VpuQp4Ov3OHwWWp/o3gf+Wlj8BfDMtLwe+M9DXco+i6EqgKSJ2REQr8AiwrMJtGg7LgNVpeTVwU+WaUl5E/BQ4VFLuq/3LgIeiaAMwRdLMs9LQAejjXPqyDHgkIloi4hWgieK/x1EhIvZGxHNp+U3gJYrfZT9W35u+zqcvo/b9Sb/jY+lpVXoEcA3wWKqXvjdd79ljwLWSNJDXclAUzQJezTzfTfl/PKNRAD+StFHSylSbERF70/LrwIzKNG3Q+mr/WH2/7kzDMasyw4Bj5lzSUMVlFP/Pdcy/NyXnA2Pw/ZGUl/Q8sB9YR7HHcyQi2tMm2faePpe0/g1g+kBex0Fx7nhfRFwO3ADcIenq7Moo9jfH7LXQY739wH3AxcAiYC/w1Yq25gxJqgMeBz4ZEUez68bie9PL+YzJ9yciOiJiETCbYk/nXSPxOg6Koj3AnMzz2ak2ZkTEnvRzP/B9iv9o9nV1+9PP/ZVr4aD01f4x935FxL70H3Un8ABvDV+M+nORVEXxj+rfRcT3UnnMvje9nc9Yfn8AIuII8BTwHygO93V9e2m2vafPJa0/Hzg4kOM7KIqeBRakqwWqKU70rKlwmwZM0iRJ53UtA9cBmymew4q02Qrgicq0cND6av8a4LZ0hc0S4I3MMMioVDJOfzPF9weK57I8XZEyD1gAPHO229eXNIb9IPBSRPx1ZtWYfG/6Op+x+P5IapA0JS1PAD5Ecc7lKeCWtFnpe9P1nt0C/CT1BvtX6Zn70fKgeLXGLyiO8X2u0u05w7bPp3hlxs+BLV3tpzj+uB7YBvwYmFbptpY5h4cpdvnbKI6r3t5X+yle7fH19F69ACyudPsHcC7fTm3dlP6DnZnZ/nPpXLYCN1S6/SXn8j6Kw0qbgOfT48Yx/N70dT5j7v0B3gP8LLV5M/D5VJ9PMcyagO8CNalem543pfXzB/pa/ggPMzMry0NPZmZWloPCzMzKclCYmVlZDgozMyvLQWFmZmU5KMzMrCwHhZmZlfX/ASSJjNc3PUeoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#不同类型的新闻出现的次数\n",
    "plt.plot(user_click_merge['category_id'].value_counts().values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb1870546d8>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#出现次数比较少的新闻类型, 有些新闻类型，基本上就出现过几次\n",
    "plt.plot(user_click_merge['category_id'].value_counts().values[150:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.630633e+06\n",
       "mean     2.043012e+02\n",
       "std      6.382198e+01\n",
       "min      0.000000e+00\n",
       "25%      1.720000e+02\n",
       "50%      1.970000e+02\n",
       "75%      2.290000e+02\n",
       "max      6.690000e+03\n",
       "Name: words_count, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#新闻字数的描述性统计\n",
    "user_click_merge['words_count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb1899594a8>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(user_click_merge['words_count'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用户点击的新闻类型的偏好\n",
    "此特征可以用于度量用户的兴趣是否广泛。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb189871da0>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sorted(user_click_merge.groupby('user_id')['category_id'].nunique(), reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>250000.000000</td>\n",
       "      <td>250000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>124999.500000</td>\n",
       "      <td>4.573188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>72168.927986</td>\n",
       "      <td>4.419800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>62499.750000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>124999.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>187499.250000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>249999.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id    category_id\n",
       "count  250000.000000  250000.000000\n",
       "mean   124999.500000       4.573188\n",
       "std     72168.927986       4.419800\n",
       "min         0.000000       1.000000\n",
       "25%     62499.750000       2.000000\n",
       "50%    124999.500000       3.000000\n",
       "75%    187499.250000       6.000000\n",
       "max    249999.000000      95.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_click_merge.groupby('user_id')['category_id'].nunique().reset_index().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用户查看文章的长度的分布\n",
    "通过统计不同用户点击新闻的平均字数，这个可以反映用户是对长文更感兴趣还是对短文更感兴趣。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb18c125dd8>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图中可以发现有一小部分人看的文章平均词数非常高，也有一小部分人看的平均文章次数非常低。\n",
    "\n",
    "大多数人偏好于阅读字数在200-400字之间的新闻。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb1899110b8>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#挑出大多数人的区间仔细看看\n",
    "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True)[1000:45000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>250000.000000</td>\n",
       "      <td>250000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>124999.500000</td>\n",
       "      <td>205.830189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>72168.927986</td>\n",
       "      <td>47.174030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>62499.750000</td>\n",
       "      <td>187.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>124999.500000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>187499.250000</td>\n",
       "      <td>217.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>249999.000000</td>\n",
       "      <td>3434.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id    words_count\n",
       "count  250000.000000  250000.000000\n",
       "mean   124999.500000     205.830189\n",
       "std     72168.927986      47.174030\n",
       "min         0.000000       8.000000\n",
       "25%     62499.750000     187.500000\n",
       "50%    124999.500000     202.000000\n",
       "75%    187499.250000     217.750000\n",
       "max    249999.000000    3434.500000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#更加详细的参数\n",
    "user_click_merge.groupby('user_id')['words_count'].mean().reset_index().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>click_environment</th>\n",
       "      <th>click_deviceGroup</th>\n",
       "      <th>click_os</th>\n",
       "      <th>click_country</th>\n",
       "      <th>click_region</th>\n",
       "      <th>click_referrer_type</th>\n",
       "      <th>rank</th>\n",
       "      <th>click_cnts</th>\n",
       "      <th>category_id</th>\n",
       "      <th>created_at_ts</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>249990</td>\n",
       "      <td>162300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>281</td>\n",
       "      <td>0.989186</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>249998</td>\n",
       "      <td>160974</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>281</td>\n",
       "      <td>0.989092</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>249985</td>\n",
       "      <td>160974</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>281</td>\n",
       "      <td>0.989092</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>249979</td>\n",
       "      <td>162300</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>281</td>\n",
       "      <td>0.989186</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>249988</td>\n",
       "      <td>160974</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>281</td>\n",
       "      <td>0.989092</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  click_article_id  click_timestamp  click_environment  \\\n",
       "18   249990            162300         0.000000                  4   \n",
       "2    249998            160974         0.000002                  4   \n",
       "30   249985            160974         0.000003                  4   \n",
       "50   249979            162300         0.000004                  4   \n",
       "25   249988            160974         0.000004                  4   \n",
       "\n",
       "    click_deviceGroup  click_os  click_country  click_region  \\\n",
       "18                  3        20              1            25   \n",
       "2                   1        12              1            13   \n",
       "30                  1        17              1             8   \n",
       "50                  1        17              1            25   \n",
       "25                  1        17              1            21   \n",
       "\n",
       "    click_referrer_type  rank  click_cnts  category_id  created_at_ts  \\\n",
       "18                    2     5           5          281       0.989186   \n",
       "2                     2     5           5          281       0.989092   \n",
       "30                    2     8           8          281       0.989092   \n",
       "50                    2     2           2          281       0.989186   \n",
       "25                    2    17          17          281       0.989092   \n",
       "\n",
       "    words_count  \n",
       "18          193  \n",
       "2           259  \n",
       "30          259  \n",
       "50          193  \n",
       "25          259  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#为了更好的可视化，这里把时间进行归一化操作\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mm = MinMaxScaler()\n",
    "user_click_merge['click_timestamp'] = mm.fit_transform(user_click_merge[['click_timestamp']])\n",
    "user_click_merge['created_at_ts'] = mm.fit_transform(user_click_merge[['created_at_ts']])\n",
    "\n",
    "user_click_merge = user_click_merge.sort_values('click_timestamp')\n",
    "user_click_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_diff_time_func(df, col):\n",
    "    df = pd.DataFrame(df, columns={col})\n",
    "    df['time_shift1'] = df[col].shift(1).fillna(0)\n",
    "    df['diff_time'] = abs(df[col] - df['time_shift1'])\n",
    "    return df['diff_time'].mean()\n",
    "# 点击时间差的平均值\n",
    "mean_diff_click_time = user_click_merge.groupby('user_id')['click_timestamp', 'created_at_ts'].apply(lambda x: mean_diff_time_func(x, 'click_timestamp'))\n",
    "plt.plot(sorted(mean_diff_click_time.values, reverse=True))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 前后点击文章的创建时间差的平均值\n",
    "mean_diff_created_time = user_click_merge.groupby('user_id')['click_timestamp', 'created_at_ts'].apply(lambda x: mean_diff_time_func(x, 'created_at_ts'))\n",
    "plt.plot(sorted(mean_diff_created_time.values, reverse=True))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以发现用户先后点击文章，文章的创建时间也是有差异的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用户前后点击文章的相似性分布\n",
    "item_idx_2_rawid_dict = dict(zip(item_emb_df['article_id'], item_emb_df.index))\n",
    "del item_emb_df['article_id']\n",
    "item_emb_np = np.ascontiguousarray(item_emb_df.values, dtype=np.float32)\n",
    "# 随机选择5个用户，查看这些用户前后查看文章的相似性\n",
    "sub_user_ids = np.random.choice(user_click_merge.user_id.unique(), size=15, replace=False)\n",
    "sub_user_info = user_click_merge[user_click_merge['user_id'].isin(sub_user_ids)]\n",
    "\n",
    "sub_user_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_item_sim_list(df):\n",
    "    sim_list = []\n",
    "    item_list = df['click_article_id'].values\n",
    "    for i in range(0, len(item_list)-1):\n",
    "        emb1 = item_emb_np[item_idx_2_rawid_dict[item_list[i]]]\n",
    "        emb2 = item_emb_np[item_idx_2_rawid_dict[item_list[i+1]]]\n",
    "        sim_list.append(np.dot(emb1,emb2)/(np.linalg.norm(emb1)*(np.linalg.norm(emb2))))\n",
    "    sim_list.append(0)\n",
    "    return sim_list\n",
    "for _, user_df in sub_user_info.groupby('user_id'):\n",
    "    item_sim_list = get_item_sim_list(user_df)\n",
    "    plt.plot(item_sim_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从图中可以看出有些用户前后看的商品的相似度波动比较大，有些波动比较小，也是有一定的区分度的。\n",
    "\n",
    "#### 总结\n",
    "通过数据分析的过程， 我们目前可以得到以下几点重要的信息， 这个对于我们进行后面的特征制作和分析非常有帮助：\n",
    "\n",
    "训练集和测试集的用户id没有重复，也就是测试集里面的用户模型是没有见过的  \n",
    "训练集中用户最少的点击文章数是2， 而测试集里面用户最少的点击文章数是1  \n",
    "用户对于文章存在重复点击的情况， 但这个都存在于训练集里面  \n",
    "同一用户的点击环境存在不唯一的情况，后面做这部分特征的时候可以采用统计特征  \n",
    "用户点击文章的次数有很大的区分度，后面可以根据这个制作衡量用户活跃度的特征  \n",
    "文章被用户点击的次数也有很大的区分度，后面可以根据这个制作衡量文章热度的特征  \n",
    "用户看的新闻，相关性是比较强的，所以往往我们判断用户是否对某篇文章感兴趣的时候， 在很大程度上会和他历史点击过的文章有关  \n",
    "用户点击的文章字数有比较大的区别， 这个可以反映用户对于文章字数的区别  \n",
    "用户点击过的文章主题也有很大的区别， 这个可以反映用户的主题偏好  \n",
    "不同用户点击文章的时间差也会有所区别， 这个可以反映用户对于文章时效性的偏好  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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